Kollektive Migration von Lungenkrebszellen - Modellierung und Datenanalyse

Für die Wundheilung von Epithelgewebe ist ein komplexes Zusammenspiel zwischen zellulärer Differenzierung, Proliferation und Migration erforderlich. Typische Kennzeichen von Krebserkrankungen sind Störungen und Unausgewogenheit in Aktivierung, Deaktivierung und Aufrechterhaltung dieser Prozesse. Deswegen werden Tumore oft als „Wunden, die niemals heilen“ beschrieben. Aktuelle Ansätze in der Krebstherapie fokussieren sich auf die Regulation von Signalübertragung, zum Beispiel die selektive Inhibierung des EGF/EGFR-Signalpfades. Unser Interesse liegt im Zusammenspiel zwischen intrazellulärer Signalübertragung und migratorischem Verhalten von nichtkleinzelligen Lungenkrebszellen (NSCLC). Wir analysieren experimentelle Daten aus Migrations-Assays mit Lungenkrebszellen nach Stimulation mit verschiedenen Wachstumsfaktoren, oder Inhibierung ausgewählter Signalpfade. Es wurde ein effizienter Arbeitsablauf mit großteils automatisierter Datenanalyse entwickelt und implementiert. Wir benutzen insbesondere Methoden der Particle Image Velocimetry (PIV) und Einzel-Zell-Tracking um migratorische Charakteristiken, wie zum Beispiel nach Zeit und Raum aufgelöste Geschwindigkeitsverteilungen oder Korrelationslängen zu berechnen und zu untersuchen, wie diese sich für unterschiedliche experimentelle Bedingungen verändern. Um das Migrationsverhalten zu simulieren verwenden wir ein Modell für kollektive Zellmigration, welches zufällige Motilität und Zell-Zell-Adhäsion beschreibt. Vergleiche zwischen Simulationen des Modells und Parametern, die aus der Datenanalyse inferiert werden können, führen zu der Schlussfolgerung, dass im Modell ein weiterer Term für gerichtete Zellbewegung benötigt wird. Das durch diesen Term erweiterte neue Modell ist in der Lage, die meisten in den Daten sichtbaren Dynamiken zu erklären. Agentenbasierte Simulationen des Modells reproduzieren migratorische Phänotypen, die in den experimentellen Daten auftreten. Durch die Stochastizität des Modells ist ein direkter Fit des erweiterten Modells an die Daten nicht möglich. Wir stellen allerdings eine Methode vor, mit der interzelluläre Parameter, die die Migration kontrollieren, direkt aus experimentellen Daten inferiert werden können. Die Methode wurde an simulierten Daten getestet und kann auf experimentelle Daten angewendet werden, um zellspezifische Parameter zu bestimmen. Die Kombination von Datenanalyse und Modellierung gewährt auf diese Weise Zugang zu einer umfangreichen Menge an Parametern, die die kollektive Migration von Zellen beschreiben. vi

Kollektive Migration von Lungenkrebszellen - Modellierung und Datenanalyse

Für die Wundheilung von Epithelgewebe ist ein komplexes Zusammenspiel zwischen zellulärer Differenzierung, Proliferation und Migration erforderlich. Typische Kennzeichen von Krebserkrankungen sind Störungen und Unausgewogenheit in Aktivierung, Deaktivierung und Aufrechterhaltung dieser Prozesse. Deswegen werden Tumore oft als „Wunden, die niemals heilen“ beschrieben. Aktuelle Ansätze in der Krebstherapie fokussieren sich auf die Regulation von Signalübertragung, zum Beispiel die selektive Inhibierung des EGF/EGFR-Signalpfades. Unser Interesse liegt im Zusammenspiel zwischen intrazellulärer Signalübertragung und migratorischem Verhalten von nichtkleinzelligen Lungenkrebszellen (NSCLC). Wir analysieren experimentelle Daten aus Migrations-Assays mit Lungenkrebszellen nach Stimulation mit verschiedenen Wachstumsfaktoren, oder Inhibierung ausgewählter Signalpfade. Es wurde ein effizienter Arbeitsablauf mit großteils automatisierter Datenanalyse entwickelt und implementiert. Wir benutzen insbesondere Methoden der Particle Image Velocimetry (PIV) und Einzel-Zell-Tracking um migratorische Charakteristiken, wie zum Beispiel nach Zeit und Raum aufgelöste Geschwindigkeitsverteilungen oder Korrelationslängen zu berechnen und zu untersuchen, wie diese sich für unterschiedliche experimentelle Bedingungen verändern. Um das Migrationsverhalten zu simulieren verwenden wir ein Modell für kollektive Zellmigration, welches zufällige Motilität und Zell-Zell-Adhäsion beschreibt. Vergleiche zwischen Simulationen des Modells und Parametern, die aus der Datenanalyse inferiert werden können, führen zu der Schlussfolgerung, dass im Modell ein weiterer Term für gerichtete Zellbewegung benötigt wird. Das durch diesen Term erweiterte neue Modell ist in der Lage, die meisten in den Daten sichtbaren Dynamiken zu erklären. Agentenbasierte Simulationen des Modells reproduzieren migratorische Phänotypen, die in den experimentellen Daten auftreten. Durch die Stochastizität des Modells ist ein direkter Fit des erweiterten Modells an die Daten nicht möglich. Wir stellen allerdings eine Methode vor, mit der interzelluläre Parameter, die die Migration kontrollieren, direkt aus experimentellen Daten inferiert werden können. Die Methode wurde an simulierten Daten getestet und kann auf experimentelle Daten angewendet werden, um zellspezifische Parameter zu bestimmen. Die Kombination von Datenanalyse und Modellierung gewährt auf diese Weise Zugang zu einer umfangreichen Menge an Parametern, die die kollektive Migration von Zellen beschreiben. vi

Oligosarcomas, IDH-mutant are distinct and aggressive

Oligodendrogliomas are defined at the molecular level by the presence of an IDH mutation and codeletion of chromosomal arms 1p and 19q. In the past, case reports and small studies described gliomas with sarcomatous features arising from oligodendrogliomas, so called oligosarcomas. Here, we report a series of 24 IDH-mutant oligosarcomas from 23 patients forming a distinct methylation class. The tumors were recurrences from prior oligodendrogliomas or developed de novo. Precursor tumors of 12 oligosarcomas were histologically and molecularly indistinguishable from conventional oligodendrogliomas. Oligosarcoma tumor cells were embedded in a dense network of reticulin fibers, frequently showing p53 accumulation, positivity for SMA and CALD1, loss of OLIG2 and gain of H3K27 trimethylation (H3K27me3) as compared to primary lesions. In 5 oligosarcomas no 1p/19q codeletion was detectable, although it was present in the primary lesions. Copy number neutral LOH was determined as underlying mechanism. Oligosarcomas harbored an increased chromosomal copy number variation load with frequent CDKN2A/B deletions. Proteomic profiling demonstrated oligosarcomas to be highly distinct from conventional CNS WHO grade 3 oligodendrogliomas with consistent evidence for a smooth muscle differentiation. Expression of several tumor suppressors was reduced with NF1 being lost frequently. In contrast, oncogenic YAP1 was aberrantly overexpressed in oligosarcomas. Panel sequencing revealed mutations in NF1 and TP53 along with IDH1/2 and TERT promoter mutations. Survival of patients was significantly poorer for oligosarcomas as first recurrence than for grade 3 oligodendrogliomas as first recurrence. These results establish oligosarcomas as a distinct group of IDH-mutant gliomas differing from conventional oligodendrogliomas on the histologic, epigenetic, proteomic, molecular and clinical level. The diagnosis can be based on the combined presence of (a) sarcomatous histology, (b) IDH-mutation and (c) TERT promoter mutation and/or 1p/19q codeletion, or, in unresolved cases, on its characteristic DNA methylation profile. Keywords: 1p/19q; Codeletion; DNA methylation; Gliosarcoma; NF1; Oligodendroglioma; Oligosarcoma; Prognosis; SMA; Subtype; TERT; TP53; Type; Variant; YAP1

Evolutionary Trajectories of IDH Glioblastomas Reveal a Common Path of Early Tumorigenesis Instigated Years ahead of Initial Diagnosis

We studied how intratumoral genetic heterogeneity shapes tumor growth and therapy response for isocitrate dehydrogenase (IDH)-wild-type glioblastoma, a rapidly regrowing tumor. We inferred the evolutionary trajectories of matched pairs of primary and relapsed tumors based on deep whole-genome-sequencing data. This analysis suggests both a distant origin of de novo glioblastoma, up to 7 years before diagnosis, and a common path of early tumorigenesis, with one or more of chromosome 7 gain, 9p loss, or 10 loss, at tumor initiation. TERT promoter mutations often occurred later as a prerequisite for rapid growth. In contrast to this common early path, relapsed tumors acquired no stereotypical pattern of mutations and typically regrew from oligoclonal origins, suggesting sparse selective pressure by therapeutic measures

Integrated Molecular-Morphologic Meningioma Classification: A Multicenter Retrospective Analysis, Retrospectively and Prospectively Validated

PURPOSE: Meningiomas are the most frequent primary intracranial tumors. Patient outcome varies widely from benign to highly aggressive, ultimately fatal courses. Reliable identification of risk of progression for individual patients is of pivotal importance. However, only biomarkers for highly aggressive tumors are established (CDKN2A/B and TERT), whereas no molecularly based stratification exists for the broad spectrum of patients with low- and intermediate-risk meningioma. METHODS: DNA methylation data and copy-number information were generated for 3,031 meningiomas (2,868 patients), and mutation data for 858 samples. DNA methylation subgroups, copy-number variations (CNVs), mutations, and WHO grading were analyzed. Prediction power for outcome was assessed in a retrospective cohort of 514 patients, validated on a retrospective cohort of 184, and on a prospective cohort of 287 multicenter cases. RESULTS: Both CNV- and methylation family-based subgrouping independently resulted in increased prediction accuracy of risk of recurrence compared with the WHO classification (c-indexes WHO 2016, CNV, and methylation family 0.699, 0.706, and 0.721, respectively). Merging all risk stratification approaches into an integrated molecular-morphologic score resulted in further substantial increase in accuracy (c-index 0.744). This integrated score consistently provided superior accuracy in all three cohorts, significantly outperforming WHO grading (c-index difference P = .005). Besides the overall stratification advantage, the integrated score separates more precisely for risk of progression at the diagnostically challenging interface of WHO grade 1 and grade 2 tumors (hazard ratio 4.34 [2.48-7.57] and 3.34 [1.28-8.72] retrospective and prospective validation cohorts, respectively). CONCLUSION: Merging these layers of histologic and molecular data into an integrated, three-tiered score significantly improves the precision in meningioma stratification. Implementation into diagnostic routine informs clinical decision making for patients with meningioma on the basis of robust outcome prediction

The miR-139-5p regulates proliferation of supratentorial paediatric low-grade gliomas by targeting the PI3K/AKT/mTORC1 signalling

Paediatric low-grade gliomas (pLGGs) are a heterogeneous group of brain tumours associated with a high overall survival: however, they are prone to recur and supratentorial lesions are difficult to resect, being associated with high percentage of disease recurrence. Our aim was to shed light on the biology of pLGGs

Reference on copy number variations in pleomorphic xanthoastrocytoma: Implications for diagnostic approach

Pleomorphic xanthoastrocytoma (PXA) poses a diagnostic challenge. The present study relies on methylation-based predictions and focuses on copy number variations (CNV) in PXA. We identified 551 tumors from patients having received the histologic diagnosis or differential diagnosis pleomorphic xanthoastrocytoma (PXA) uploaded to the web page www.molecularneuropathology.org. Of these 551 tumors, 165 received the prediction “methylation class (anaplastic) pleomorphic xanthoastrocytoma” with a calibrated score >=0.9 by the brain tumor classifier version v12.8 and, therefore, were defined the PXA reference set designated mcPXAref. In addition to these 165 mcPXAref, 767 other tumors received the prediction mcPXA with a calibrated score >=0.9 but without a histological PXA diagnosis. The total number of individual tumors predicted by histology and/or by methylome based classification as PXA, mcPXA or both was 1318, and these were designated the study cohort. The selection of a control cohort was guided by methylation-based predictions recurrently observed for the other 386/551 tumors diagnosed as histologic PXA. 131/386 received predictions for another entity besides PXA with a score >=0.9. Control tumors corresponding to the 11 most common other predictions were selected, adding up to 1100 reference cases. CNV profiles were calculated from all methylation datasets of the study and control cohorts. Special attention was given to the 7/10 signature, gene amplifications and homozygous deletion of CDKN2A/B. Comparison of CNV in the subsets of the study cohort and the control cohort were used to establish relations independent of histological diagnoses. Tumors in mcPXA were highly homogenous in regard to CNV alterations, irrespective of the histological diagnoses. The 7/10 signature commonly present in glioblastoma, IDH-wildtype, was present in 15-20% of mcPXA, whereas amplification of oncogenes (likewise common in glioblastoma) was very rare in mcPXA (<1%). In contrast, the histology-based PXA group exhibited high variance in regard to methylation classes as well as to CNVs. Our data add to the notion, that histologically defined PXA likely only represent a subset of the biological disease

Clinicopathologic and molecular analysis of embryonal rhabdomyosarcoma of the genitourinary tract: evidence for a distinct DICER1-associated subgroup.

Embryonal rhabdomyosarcoma (ERMS) of the uterus has recently been shown to frequently harbor DICER1 mutations. Interestingly, only rare cases of extrauterine DICER1-associated ERMS, mostly located in the genitourinary tract, have been reported to date. Our goal was to study clinicopathologic and molecular profiles of DICER1-mutant (DICER1-mut) and DICER1-wild type (DICER1-wt) ERMS in a cohort of genitourinary tumors. We collected a cohort of 17 ERMS including nine uterine (four uterine corpus and five cervix), one vaginal, and seven urinary tract tumors. DNA sequencing revealed mutations of DICER1 in 9/9 uterine ERMS. All other ERMS of our cohort were DICER1-wt. The median age at diagnosis of patients with DICER1-mut and DICER1-wt ERMS was 36 years and 5 years, respectively. Limited follow-up data (available for 15/17 patients) suggested that DICER1-mut ERMS might show a less aggressive clinical course than DICER1-wt ERMS. Histological features only observed in DICER1-mut ERMS were cartilaginous nodules (6/9 DICER1-mut ERMS), in one case accompanied by foci of ossification. Recurrent mutations identified in both DICER1-mut and DICER1-wt ERMS affected KRAS, NRAS, and TP53. Copy number analysis revealed similar structural variations with frequent gains on chromosomes 2, 3, and 8, independent of DICER1 mutation status. Unsupervised hierarchical clustering of array-based whole-genome DNA methylation data of our study cohort together with an extended methylation data set including different RMS subtypes from genitourinary and extra-genitourinary locations (n = 102), revealed a distinct cluster for DICER1-mut ERMS. Such tumors clearly segregated from the clusters of DICER1-wt ERMS, alveolar RMS, and MYOD1-mutant spindle cell and sclerosing RMS. Only one tumor, previously diagnosed as ERMS arising in the maxilla of a 6-year-old boy clustered with DICER1-mut ERMS of the uterus. Subsequent sequencing analysis identified two DICER1 mutations in the latter case. Our results suggest that DICER1-mut ERMS might qualify as a distinct subtype in future classifications of RMS

Molecular profiling of pediatric meningiomas shows tumor characteristics distinct from adult meningiomas

In contrast to adults, meningiomas are uncommon tumors in childhood and adolescence. Whether adult and pediatric meningiomas differ on a molecular level is unclear. Here we report detailed genomic analyses of 37 pediatric meningiomas by sequencing and DNA methylation profiling. Histologically, the series was dominated by meningioma subtypes with aggressive behavior, with 70% of patients suffering from WHO grade II or III meningiomas. The most frequent cytogenetic aberrations were loss of chromosomes 22 (23/37 [62%]), 1 (9/37 [24%]), 18 (7/37 [19%]), and 14 (5/37 [14%]). Tumors with NF2 alterations exhibited overall increased chromosomal instability. Unsupervised clustering of DNA methylation profiles revealed separation into three groups: designated group 1 composed of clear cell and papillary meningiomas, whereas group 2A comprised predominantly atypical meningiomas and group 2B enriched for rare high-grade subtypes (rhabdoid, chordoid). Meningiomas from NF2 patients clustered exclusively within groups 1 and 2A. When compared with a dataset of 105 adult meningiomas, the pediatric meningiomas largely grouped separately. Targeted panel DNA sequencing of 34 tumors revealed frequent NF2 alterations, while other typical alterations found in adult non-NF2 tumors were absent. These data demonstrate that pediatric meningiomas are characterized by molecular features distinct from adult tumors