Top ten discoveries of the year: Neurooncology

This article briefly discusses 10 topics that were selected by the author as top 10 discoveries published in 2019 in the broader field of neuro-oncological pathology (so including neurosciences as well as clinical neuro-oncology but with implications for neuro-oncological pathology). Some topics concern new information on immunohistochemical and molecular markers that enable improved diagnosis of particular tumors of the central nervous system (CNS) and information on a refined classification of medulloblastomas. Subsequently, several papers are discussed that further elucidate some pathobiological aspects of especially medulloblastomas (histogenesis, molecular evolution) and diffuse gliomas (mechanisms involved in CNS infiltration, role of cancer stem(-like) cells, longitudinal molecular evolution). The remaining topics concern progress made in vaccination therapy for glioblastomas and in using cerebrospinal fluid for liquid biopsy diagnosis of gliomas. Clearly, substantial, and sometimes even amazing progress has been made in increasing our understanding in several areas of neuro-oncological pathology. At the same time, almost every finding raises new questions, and translation of new insights in improving the outcome for patients suffering from CNS tumors remains a huge challenge

Network-based methods for biological data integration in precision medicine

[eng] The vast and continuously increasing volume of available biomedical data produced during the last decades opens new opportunities for large-scale modeling of disease biology, facilitating a more comprehensive and integrative understanding of its processes. Nevertheless, this type of modelling requires highly efficient computational systems capable of dealing with such levels of data volumes. Computational approximations commonly used in machine learning and data analysis, namely dimensionality reduction and network-based approaches, have been developed with the goal of effectively integrating biomedical data. Among these methods, network-based machine learning stands out due to its major advantage in terms of biomedical interpretability. These methodologies provide a highly intuitive framework for the integration and modelling of biological processes. This PhD thesis aims to explore the potential of integration of complementary available biomedical knowledge with patient-specific data to provide novel computational approaches to solve biomedical scenarios characterized by data scarcity. The primary focus is on studying how high-order graph analysis (i.e., community detection in multiplex and multilayer networks) may help elucidate the interplay of different types of data in contexts where statistical power is heavily impacted by small sample sizes, such as rare diseases and precision oncology. The central focus of this thesis is to illustrate how network biology, among the several data integration approaches with the potential to achieve this task, can play a pivotal role in addressing this challenge provided its advantages in molecular interpretability. Through its insights and methodologies, it introduces how network biology, and in particular, models based on multilayer networks, facilitates bringing the vision of precision medicine to these complex scenarios, providing a natural approach for the discovery of new biomedical relationships that overcomes the difficulties for the study of cohorts presenting limited sample sizes (data-scarce scenarios). Delving into the potential of current artificial intelligence (AI) and network biology applications to address data granularity issues in the precision medicine field, this PhD thesis presents pivotal research works, based on multilayer networks, for the analysis of two rare disease scenarios with specific data granularities, effectively overcoming the classical constraints hindering rare disease and precision oncology research. The first research article presents a personalized medicine study of the molecular determinants of severity in congenital myasthenic syndromes (CMS), a group of rare disorders of the neuromuscular junction (NMJ). The analysis of severity in rare diseases, despite its importance, is typically neglected due to data availability. In this study, modelling of biomedical knowledge via multilayer networks allowed understanding the functional implications of individual mutations in the cohort under study, as well as their relationships with the causal mutations of the disease and the different levels of severity observed. Moreover, the study presents experimental evidence of the role of a previously unsuspected gene in NMJ activity, validating the hypothetical role predicted using the newly introduced methodologies. The second research article focuses on the applicability of multilayer networks for gene priorization. Enhancing concepts for the analysis of different data granularities firstly introduced in the previous article, the presented research provides a methodology based on the persistency of network community structures in a range of modularity resolution, effectively providing a new framework for gene priorization for patient stratification. In summary, this PhD thesis presents major advances on the use of multilayer network-based approaches for the application of precision medicine to data-scarce scenarios, exploring the potential of integrating extensive available biomedical knowledge with patient-specific data

Molecular Targets of CNS Tumors

Molecular Targets of CNS Tumors is a selected review of Central Nervous System (CNS) tumors with particular emphasis on signaling pathway of the most common CNS tumor types. To develop drugs which specifically attack the cancer cells requires an understanding of the distinct characteristics of those cells. Additional detailed information is provided on selected signal pathways in CNS tumors

Analysis of Oncogenic Drivers in Supratentorial Brain Tumors

Pediatric brain tumors are a leading cause of cancer mortality among children and adolescents (age 0-19) because of the paucity of effective treatment regimens. Especially for ependymoma, surgical intervention combined with focal radiotherapy is the current standard of care in routine clinical practice while this regimen very often induces irreversible damage on the developing brain and patients frequently still suffer from tumor recurrence. Thus, generating de novo representative tumor models to decipher the underlying molecular mechanisms of tumorigenesis is imminent and crucial to provide more precise and mechanism-of-action based treatment plans. In my thesis, I applied various techniques to create in vivo models for several brain tumor types and identified potential therapeutic vulnerabilities. Chapter 2 focuses on dissecting the role of oncogenic fusion genes in C11orf95 fusion- positive supratentorial ependymoma (ST-EPN), a type of pediatric brain tumor with poor prognosis. C11orf95 is a zinc finger protein that binds to DNA but has not yet been well characterized. I performed in-utero electroporation in mouse embryos and found all tested C11orf95 fusion genes were able to drive malignant transformation in the cerebral cortex. The tumors faithfully recapitulated molecular characteristics of their human counterparts. The zinc finger domain and the fusion partners were essential for tumor formation. Cross-species genomic analyses demonstrated that C11orf95-related fusions can increase the expression of a sonic hedgehog mediator gene, GLI2. Targeting GLI2 with arsenic trioxide prolonged survival in mouse models, providing a basis for further preclinical studies for C11orf95 fusion-positive tumors. Based on these findings, C11orf95 is now officially designated as zinc finger translocation associated (ZFTA) by the HUGO Gene Nomenclature Committee. In the latest edition of the WHO classification of central nervous tumors, the group of ST-EPN with ZFTA fusion genes is now named as Supratentorial ependymoma, ZFTA fusion-positive (ST- EPN-ZFTA). In Chapter 3, I investigated on a novel group of neuroepithelial tumors harboring PLAGL1 fusion (NET_PLAGL1) that has been identified in 2021 only. Mouse model generation via in-utero electroporation unfortunately failed. However, after I had performed substantial methodological optimization, overexpression of PLAGL1 fusion gene through a doxycycline-mediated system in human induced pluripotent stem cell-derived neural stem cells, followed by in vivo orthotopic transplantation successfully led to brain tumor formation in mice. This inducible in vivo system offers a reliable model to study NET_PLAGL1 tumors as well as a versatile tool to answer various biological questions behind brain tumorigenesis.Array-based DNA methylation analysis to accurately classify tumors has been developed as a routine diagnostic tool for brain tumors and sarcomas. Since mouse models are the most widely used in vivo systems in pediatric cancer research, it is important to assess the molecular similarity across species based on the methylome. In Chapter 4, I describe the approach of generating a mouse model biobank for pediatric cancers. I collected and profiled 86 murine tumor models and 40 normal tissue controls. DNA methylation-based clustering showed that samples from the same model clustered together and the copy number alteration pattern of ependymoma and glioma (e.g TFG-MET fusion-driven) mouse models recapitulate their human counterparts. This validated biobank will serve as a beneficial resource for future developmental studies such as identifying cellular origin of the tumor and decoding the composition of tumor immune microenvironment

Learning Latent Variable Gaussian Graphical Model for Biomolecular Network with Low Sample Complexity

Learning a Gaussian graphical model with latent variables is ill posed when there is insufficient sample complexity, thus having to be appropriately regularized. A common choice is convex l1 plus nuclear norm to regularize the searching process. However, the best estimator performance is not always achieved with these additive convex regularizations, especially when the sample complexity is low. In this paper, we consider a concave additive regularization which does not require the strong irrepresentable condition. We use concave regularization to correct the intrinsic estimation biases from Lasso and nuclear penalty as well. We establish the proximity operators for our concave regularizations, respectively, which induces sparsity and low rankness. In addition, we extend our method to also allow the decomposition of fused structure-sparsity plus low rankness, providing a powerful tool for models with temporal information. Specifically, we develop a nontrivial modified alternating direction method of multipliers with at least local convergence. Finally, we use both synthetic and real data to validate the excellence of our method. In the application of reconstructing two-stage cancer networks, “the Warburg effect” can be revealed directly

The Protumorigenic Role of Caspase-8 in Neuroblastoma

Neuroblastoma (NB), the most common extracranial solid tumor in children, accounts for 15% of cancer-related deaths in pediatric patients. Caspase-8 (casp8), a proapoptotic protein, is silenced in approximately, 50-70% of neuroblastoma patient samples. Loss of casp8 has been suggested to increase NB metastasis and correlated, in some studies, with advanced-stage NB. Furthermore, decreased casp8 expression may facilitate neuroblastoma tumorigenesis by protecting cells from cell death mediated by either integrins or chemotherapeutics. Paradoxically, casp8 expression is maintained in 30-50% of NB patient samples giving rise to the possibility that casp8 may provide selective advantages for NB tumorigenesis. Caspase-8 is shown to increase tumor cell adhesion, migration and growth. This drives the question as to how a single protein can promote very opposing functions. One possible explanation is that post- translational modifications may decrease the catalytic activity of casp8 and shift its role towards survival. Caspase-8 has been shown to be phosphorylated by SRC, thus decreasing its apoptotic function and increasing ERK signaling, which supports our hypothesis. Ataxia telangiectasia mutated kinase (ATM), an essential mediator in the DNA double strand break repair mechanism, is activated by chemotherapeutic treatments, radiation, and cellular stress. Caspase-8 has one consensus phosphorylation site that has been reported to be phosphorylated in a genome-wide screen for ATM substrates. Here, we tested whether casp8 expression enhanced primary NB tumorigenesis and whether modulation of casp8 by phosphorylation altered its biological function in NB. For the first time, this study demonstrates that casp8 enhances orthotopic xenograft tumor establishment in low tumorigenic neuroblastoma cells; which, may explain why casp8 expression is maintained in some NB tumors. Furthermore, exposure to DNA damaging agents suppresses the apoptotic function of casp8 via ATM-mediated phosphorylation, thereby shifting the balance between the proapoptotic and prosurvival functions towards cell survival. This outlines a possible mechanism by which tumor cells may avoid cell death when exposed to chemotherapeutic agents

Receptor tyrosine kinase gene copy numbers and protein expression in astrocytic brain tumors : With special reference to KIT, PDGFRA, VEGFR2 and EGFR

In the present study we investigated expression and amplification of KIT, PDGFRA, VEGFR2 and EGFR in glioblastomas and in lower grade gliomas, and analyzed the hot spot mutation sites of KIT, PDGFRA and EGFR genes for presence of mutations in glioblastoma. Furthermore, we evaluated expression of KIT, SCF and VEGFR2 in paediatric brain tumors and in tumour endothelial cells, and studied the intratumoral heterogeneity of EGFR and KIT amplifications in primary glioblastomas and astrocytomas. Mutations turned out to be infrequent in these genes suggesting that neither primary nor secondary glioblastomas are usually driven by KIT or PDGFRA mutations, or by EGFR kinase domain mutations. Amplifications of KIT, VEGFR2, PDGFRA and EGFR turned out to be frequent in glioblastoma. KIT was amplified in 47% and VEGFR2 in 39% out of the 43 primary glioblastomas investigated, and PDGFRA in 29%. Presence of KIT, PDGFRA and VEGFR2 amplifications were strongly associated (p < 0.0001 for each pair wise comparison) suggesting co-amplification. We investigated presence of gene amplifications also in other types of gliomas either in tumour samples collected at the time of the diagnosis or in samples collected at the time of tumour recurrence. In tumour tissue samples collected at the time of the diagnosis KIT and PDGFRA amplifications turned out to be more frequent in anaplastic astrocytomas than in astrocytomas, oligodendrogliomas and oligoastrocytomas. Amplified KIT was more frequently present in recurrent gliomas than in newly diagnosed. Pilocytic astrocytomas studied did not harbour amplification of KIT. KIT expression was common in tumour endothelial cells in pilocytic astrocytomas, and endothelial cell KIT was frequently activated. Tumour endothelial cell KIT expression was associated with a young age at the time of the diagnosis. Ependymomas also frequently expressed KIT in endothelial cells, and its expression tended to be associated with a young age at the time of the diagnosis. Finally, we investigated heterogeneity of KIT and EGFR amplification and their protein products in gliomas by studying several tissue blocks from each tumour. EGFR amplification was found in ten out of the 15 glioblastomas studied when analysis was carried out from only one tissue block, and in 11 cases when all available tissue blocks were analyzed. KIT was amplified in six out of the 15 index glioblastoma tissue blocks, but in 10 glioblastomas when all tissue blocks were analyzed. These findings suggest that glioblastomas show marked heterogeneity in KIT amplifications and that heterogeneity is less for EGFR amplifications.Väitöskirjatyössä olen selvittänyt KIT, PDGFRA, VEGFR2, ja EGFR geenien merkitystä ja proteiinituotteiden merkitystä diffuusisti kasvavissa aikuisten glioomissa ja glioblastoomissa sekä lasten aivosyövissä. Ko. geenien monistumien ilmeneminen vaihtelee glioomissa ja glioblastoomissa siten, että ne ovat yleensäkin yleisempiä jo pidemmälle edenneissä kasvaimissa. Lisäksi glioblastoomien alatyypeissä on havaittavissa selkeää heterogeenisyyttä EGFR ja KIT geenien suhteen: primaareissa glioblastoomissa on enemmän EGFR geenin monistumia, kun taas sekundaarisissa glioblastoomissa on enemmän KIT, PDGFR ja VEGFR2 geenin monistumia. Lisäksi primaarit glioblastomat voivat olla hyvinkin heterogeenisia KIT ja EGFR monistumien suhteen: laajan EGFR monistumia sisältävän ja EGFR proteiinia ilmentävän alueen sisällä saattaa olla pieniä populaatioita KIT monistuneita soluja, jotka yli-ilmentävät KIT proteiinia. Tällä tiedolla saattaa olla merkitystä etenkin kehitettäessä EGFR ja KIT inhibiittoreita gliobalstoomien hoitoon ja tutkittaessa niillä saatuja hoitovasteita ja uusiutumia. Lasten aivosyövissä kuten pilosyyttisissä astrosytoomissa ei ole KIT tai EGFR monistumia tai tuumorisoluissa proteiinien yli-ilmentymistä. KIT proteiinin ilmentyminen oli kuitenkin merkittävää pilosyyttisten astrosytoomien kasvaimen uudisverisuonissa varsinkin nuoremmilla (noin 0-10 vuotiailla) potilailla. Sama trendi oli havaittavissa myös tutkimissamme ependymoomissa. Pilosyyttiset astrosytoomat ovat erittäin hyvälaatuisia ja leikattavissa, mutta mikäli kasvainta ei pystytä leikkauksella poistamaan, se uusiutuu. Mielenkiintoista on erityisesti se, että nämä hyvänlaatuiset lasten aivosyövät omaavat samoja piirteitä (KIT proteiinin yli-ilmentyminen) uudisverisuonissaan kuin erittäin pahanlaatuiset glioblastoomatkin

Brain tumors: preclinical imaging and novel therapies

Vandertop, W.P. [Promotor]Würdinger, T. [Promotor]Noske, D.P. [Copromotor]Hulleman, E. [Copromotor

Molecular diagnostic tools for the World Health Organization (WHO) 2021 classification of gliomas, glioneuronal and neuronal tumors; an EANO guideline

In the 5th edition of the WHO CNS tumor classification (CNS5, 2021), multiple molecular characteristics became essential diagnostic criteria for many additional CNS tumor types. For those tumors, an integrated, 'histomolecular' diagnosis is required. A variety of approaches exists for determining the status of the underyling molecular markers. The present guideline focuses on the methods that can be used for assessment of the currently most informative diagnostic and prognostic molecular markers for the diagnosis of gliomas, glioneuronal and neuronal tumors. The main characteristics of the molecular methods are systematically discussed, followed by recommendations and information on available evidence levels for diagnostic measures. The recommendations cover DNA and RNA next-generation-sequencing, methylome profiling, and select assays for single/limited target analysis, including immunohistochemistry. Additionally, because of its importance as a predictive marker in IDH-wildtype glioblastomas, tools for the analysis of MGMT promoter status are covered. A structured overview of the different assays with their characteristics, especially their advantages and limitations, is provided, and requirements for input material and reporting of results are clarified. General aspects of molecular diagnostic testing regarding clinical relevance, accessibility, cost, implementation, regulatory and ethical aspects are discussed as well. Finally, we provide an outlook on new developments in the landscape of molecular testing technologies in neuro-oncology