Patterns of Tumor Progression Predict Small and Tissue-Specific Tumor-Originating Niches

The development of cancer is a multistep process in which cells increase in malignancy through progressive alterations. Such altered cells compete with wild-type cells and have to establish within a tissue in order to induce tumor formation. The range of this competition and the tumor-originating cell type which acquires the first alteration is unknown for most human tissues, mainly because the involved processes are hardly observable, aggravating an understanding of early tumor development. On the tissue scale, one observes different progression types, namely with and without detectable benign precursor stages. Human epidemiological data on the ratios of the two progression types exhibit large differences between cancers. The idea of this study is to utilize data of the ratios of progression types in human cancers to estimate the homeostatic range of competition in human tissues. This homeostatic competition range can be interpreted as necessary numbers of altered cells to induce tumor formation on the tissue scale. For this purpose, we develop a cell-based stochastic model which is calibrated with newly-interpreted human epidemiological data. We find that the number of tumor cells which inevitably leads to later tumor formation is surprisingly small compared to the overall tumor and largely depends on the human tissue type. This result points toward the existence of a tissue-specific tumor-originating niche in which the fate of tumor development is decided early and long before a tumor becomes detectable. Moreover, our results suggest that the fixation of tumor cells in the tumor-originating niche triggers new processes which accelerate tumor growth after normal tissue homeostasis is voided. Our estimate for the human colon agrees well with the size of the stem cell niche in colonic crypts. For other tissues, our results might aid to identify the tumor-originating cell type. For instance, data on primary and secondary glioblastoma suggest that the tumors originate from a cell type competing in a range of 300 – 1,900 cells

Reconstructing the molecular life history of gliomas.

At the time of their clinical manifestation, the heterogeneous group of adult and pediatric gliomas carries a wide range of diverse somatic genomic alterations, ranging from somatic single-nucleotide variants to structural chromosomal rearrangements. Somatic abnormalities may have functional consequences, such as a decrease, increase or change in mRNA transcripts, and cells pay a penalty for maintaining them. These abnormalities, therefore, must provide cells with a competitive advantage to become engrained into the glioma genome. Here, we propose a model of gliomagenesis consisting of the following five consecutive phases that glioma cells have traversed prior to clinical manifestation: (I) initial growth; (II) oncogene-induced senescence; (III) stressed growth; (IV) replicative senescence/crisis; (V) immortal growth. We have integrated the findings from a large number of studies in biology and (neuro)oncology and relate somatic alterations and other results discussed in these papers to each of these five phases. Understanding the story that each glioma tells at presentation may ultimately facilitate the design of novel, more effective therapeutic approaches. Acta Neuropathol 2018 May; 135(5):649-670

Model-Based Evaluation of Spontaneous Tumor Regression in Pilocytic Astrocytoma

<div><p>Pilocytic astrocytoma (PA) is the most common brain tumor in children. This tumor is usually benign and has a good prognosis. Total resection is the treatment of choice and will cure the majority of patients. However, often only partial resection is possible due to the location of the tumor. In that case, spontaneous regression, regrowth, or progression to a more aggressive form have been observed. The dependency between the residual tumor size and spontaneous regression is not understood yet. Therefore, the prognosis is largely unpredictable and there is controversy regarding the management of patients for whom complete resection cannot be achieved. Strategies span from pure observation (wait and see) to combinations of surgery, adjuvant chemotherapy, and radiotherapy. Here, we introduce a mathematical model to investigate the growth and progression behavior of PA. In particular, we propose a Markov chain model incorporating cell proliferation and death as well as mutations. Our model analysis shows that the tumor behavior after partial resection is essentially determined by a risk coefficient <i>γ</i>, which can be deduced from epidemiological data about PA. Our results quantitatively predict the regression probability of a partially resected benign PA given the residual tumor size and lead to the hypothesis that this dependency is linear, implying that removing any amount of tumor mass will improve prognosis. This finding stands in contrast to diffuse malignant glioma where an extent of resection threshold has been experimentally shown, below which no benefit for survival is expected. These results have important implications for future therapeutic studies in PA that should include residual tumor volume as a prognostic factor.</p></div

Jahresbericht 2015 zur kooperativen DV-Versorgung

:VORWORT 9 ÜBERSICHT DER INSERENTEN 10 TEIL I ZUR ARBEIT DES IT-LENKUNGSAUSSCHUSSES 15 ZUR ARBEIT DES ERWEITERTEN IT-LENKUNGSAUSSCHUSSES 15 TEIL II 1 DAS ZENTRUM FÜR INFORMATIONSDIENSTE UND HOCHLEISTUNGSRECHNEN (ZIH) 19 1.1 AUFGABEN 19 1.2 ZAHLEN UND FAKTEN (REPRÄSENTATIVE AUSWAHL) 19 1.3 HAUSHALT 20 1.4 ZUR ARBEIT DES WISSENSCHAFTLICHEN BEIRATES 21 1.5 STRUKTUR / PERSONAL 22 1.6 STANDORTE 23 1.7 GREMIENARBEIT 24 2 KOMMUNIKATIONSINFRASTRUKTUR 27 2.1 NUTZUNGSÜBERSICHT NETZDIENSTE 27 2.2 NETZWERKINFRASTRUKTUR 27 2.3 KOMMUNIKATIONS- UND INFORMATIONSDIENSTE 37 3 ZENTRALES DIENSTEANGEBOT 47 3.1 SERVICE DESK 47 3.2 TROUBLE TICKET SYSTEM (OTRS) 48 3.3 IDENTITÄTSMANAGEMENT 49 3.4 LOGIN-SERVICE 51 3.5 BEREITSTELLUNG VON VIRTUELLEN SERVERN 51 3.6 STORAGE-MANAGEMENT 52 3.7 PC-POOLS 59 3.8 SECURITY 60 3.9 LIZENZ-SERVICE 61 3.10 PERIPHERIE-SERVICE 61 3.11 DRESDEN SCIENCE CALENDAR 61 4 SERVICELEISTUNGEN FÜR DEZENTRALE DV-SYSTEME 63 4.1 ALLGEMEINES 63 4.2 INVESTBERATUNG 63 4.3 PC- UND DRUCKER-SUPPORT 63 4.4 MICROSOFT WINDOWS-SUPPORT 63 4.5 ZENTRALE SOFTWARE-BESCHAFFUNG FÜR DIE TU DRESDEN 68 5 HOCHLEISTUNGSRECHNEN 71 5.1 HOCHLEISTUNGSRECHNER/SPEICHERKOMPLEX 72 5.2 NUTZUNGSÜBERSICHT DER HPC-SERVER 76 5.3 SPEZIALRESSOURCEN 76 5.4 GRID-RESSOURCEN 77 5.5 ANWENDUNGSSOFTWARE 78 5.6 VISUALISIERUNG 79 5.7 PARALLELE PROGRAMMIERWERKZEUGE 79 6 WISSENSCHAFTLICHE PROJEKTE UND KOOPERATIONEN 81 6.1 KOMPETENZZENTRUM FÜR VIDEOKONFERENZDIENSTE 81 6.2 SKALIERBARE SOFTWARE-WERKZEUGE ZUR UNTERSTÜTZUNG DER ANWENDUNGSOPTIMIERUNG AUF HPC-SYSTEMEN 81 6.3 LEISTUNGS- UND ENERGIEEFFIZIENZ-ANALYSE FÜR INNOVATIVE RECHNERARCHITEKTUREN 82 6.4 DATENINTENSIVES RECHNEN, VERTEILTES RECHNEN UND CLOUD COMPUTING 86 6.5 DATENANALYSE, METHODEN UND MODELLIERUNG IN DEN LIFE SCIENCES 88 6.6 PARALLELE PROGRAMMIERUNG, ALGORITHMEN UND METHODEN 90 6.7 INITIATIVBUDGET ZUR UNTERSTÜTZUNG VON KOOPERATIONSAUFGABEN DER SÄCHSISCHEN HOCHSCHULEN 91 6.8 KOOPERATIONEN 93 7 AUSBILDUNGSBETRIEB UND PRAKTIKA 95 7.1 AUSBILDUNG ZUM FACHINFORMATIKER / FACHRICHTUNG ANWENDUNGSENTWICKLUNG 95 7.2 PRAKTIKA 95 8 VERANSTALTUNGEN 97 8.1 AUS- UND WEITERBILDUNGSVERANSTALTUNGEN 97 8.2 NUTZERSCHULUNGEN 98 8.3 ZIH-KOLLOQUIEN 98 8.4 WORKSHOPS 98 8.5 STANDPRÄSENTATIONEN/VORTRÄGE/FÜHRUNGEN 98 9 PUBLIKATIONEN 99 TEIL III BEREICH MATHEMATIK UND NATURWISSENSCHAFTEN 105 BEREICH GEISTES- UND SOZIALWISSENSCHAFTEN 127 BEREICH INGENIEURWISSENSCHAFTEN 159 BEREICH BAU UND UMWELT 167 BEREICH MEDIZIN 18