A for-loop is all you need. For solving the inverse problem in the case of personalized tumor growth modeling

Solving the inverse problem is the key step in evaluating the capacity of a physical model to describe real phenomena. In medical image computing, it aligns with the classical theme of image-based model personalization. Traditionally, a solution to the problem is obtained by performing either sampling or variational inference based methods. Both approaches aim to identify a set of free physical model parameters that results in a simulation best matching an empirical observation. When applied to brain tumor modeling, one of the instances of image-based model personalization in medical image computing, the overarching drawback of the methods is the time complexity of finding such a set. In a clinical setting with limited time between imaging and diagnosis or even intervention, this time complexity may prove critical. As the history of quantitative science is the history of compression (Schmidhuber and Fridman, 2018), we align in this paper with the historical tendency and propose a method compressing complex traditional strategies for solving an inverse problem into a simple database query task. We evaluated different ways of performing the database query task assessing the trade-off between accuracy and execution time. On the exemplary task of brain tumor growth modeling, we prove that the proposed method achieves one order speed-up compared to existing approaches for solving the inverse problem. The resulting compute time offers critical means for relying on more complex and, hence, realistic models, for integrating image preprocessing and inverse modeling even deeper, or for implementing the current model into a clinical workflow. The code is available at https://github.com/IvanEz/for-loop-tumor

Micro-RNA networks in T-cell prolymphocytic leukemia reflect T-cell activation and shape DNA damage response and survival pathways

T-cell prolymphocytic leukemia (T-PLL) is a poor-prognostic mature T-cell malignancy. It typically presents with exponentially rising lymphocyte counts, splenomegaly, and bone marrow infiltration. Effective treatment options are scarce and a better understanding of T-PLL's pathogenesis is desirable. Activation of the TCL1 proto-oncogene and loss-of-function perturbations of the tumor suppressor ATM are T-PLL's genomic hallmarks. The leukemic cell reveals a phenotype of active T-cell receptor (TCR) signaling and aberrant DNA-damage responses. Regulatory networks based on the profile of micro-RNAs (miRs) have not been described for T-PLL. In a combined approach of small-RNA and transcriptome sequencing in 46 clinically and moleculary well-characterized T-PLL, we identified a global T-PLL-specific miR expression profile that involves 34 significantly deregulated miR species. This pattern strikingly resembled miR-ome signatures of TCR-activated T-cells. By integrating these T-PLL miR profiles with transcriptome data, we uncovered regulatory networks associated with cell survival signaling and DNA-damage response pathways. Despite a miR-ome that discerned leukemic from normal T-cells, there were also robust subsets of T-PLL defined by a small set of specific miRs. Most prominently, miR-141 and the miR-200c-cluster separated cases into two major subgroups. Furthermore, increased expression of miR-223-3p as well as reduced expression of miR-21 and the miR-29 cluster were associated with more activated T-cell phenotypes and more aggressive disease presentations. Based on the implicated pathobiological role of these miR deregulations, targeting strategies around their effectors appear worth pursuing. We also established a combinatorial miR-based overall survival score for T-PLL (miROS-T-PLL), that might improve current clinical stratifications

In vivo migration of labeled autologous natural killer cells to liver metastases in patients with colon carcinoma

BACKGROUND: Besides being the effectors of native anti-tumor cytotoxicity, NK cells participate in T-lymphocyte responses by promoting the maturation of dendritic cells (DC). Adherent NK (A-NK) cells constitute a subset of IL-2-stimulated NK cells which show increased expression of integrins and the ability to adhere to solid surface and to migrate, infiltrate, and destroy cancer. A critical issue in therapy of metastatic disease is the optimization of NK cell migration to tumor tissues and their persistence therein. This study compares localization to liver metastases of autologous A-NK cells administered via the systemic (intravenous, i.v.) versus locoregional (intraarterial, i.a.) routes. PATIENTS AND METHODS: A-NK cells expanded ex-vivo with IL-2 and labeled with (111)In-oxine were injected i.a. in the liver of three colon carcinoma patients. After 30 days, each patient had a new preparation of (111)In-A-NK cells injected i.v. Migration of these cells to various organs was evaluated by SPET and their differential localization to normal and neoplastic liver was demonstrated after i.v. injection of (99m)Tc-phytate. RESULTS: A-NK cells expressed a donor-dependent CD56(+)CD16(+)CD3(- )(NK) or CD56(+)CD16(+)CD3(+ )(NKT) phenotype. When injected i.v., these cells localized to the lung before being visible in the spleen and liver. By contrast, localization of i.a. injected A-NK cells was virtually confined to the spleen and liver. Binding of A-NK cells to liver neoplastic tissues was observed only after i.a. injections. CONCLUSION: This unique study design demonstrates that A-NK cells adoptively transferred to the liver via the intraarterial route have preferential access and substantial accumulation to the tumor site

Cell type-specific transcriptomics of esophageal adenocarcinoma as a scalable alternative for single cell transcriptomics

Single-cell transcriptomics have revolutionized our understanding of the cell composition of tumors and allowed us to identify new subtypes of cells. Despite rapid technological advancements, single-cell analysis remains resource-intense hampering the scalability that is required to profile a sufficient number of samples for clinical associations. Therefore, more scalable approaches are needed to understand the contribution of individual cell types to the development and treatment response of solid tumors such as esophageal adenocarcinoma where comprehensive genomic studies have only led to a small number of targeted therapies. Due to the limited treatment options and late diagnosis, esophageal adenocarcinoma has a poor prognosis. Understanding the interaction between and dysfunction of individual cell populations provides an opportunity for the development of new interventions. In an attempt to address the technological and clinical needs, we developed a protocol for the separation of esophageal carcinoma tissue into leukocytes (CD45+), epithelial cells (EpCAM+), and fibroblasts (two out of PDGFRα, CD90, anti-fibroblast) by fluorescence-activated cell sorting and subsequent RNA sequencing. We confirm successful separation of the three cell populations by mapping their transcriptomic profiles to reference cell lineage expression data. Gene-level analysis further supports the isolation of individual cell populations with high expression of CD3, CD4, CD8, CD19, and CD20 for leukocytes, CDH1 and MUC1 for epithelial cells, and FAP, SMA, COL1A1, and COL3A1 for fibroblasts. As a proof of concept, we profiled tumor samples of nine patients and explored expression differences in the three cell populations between tumor and normal tissue. Interestingly, we found that angiogenesis-related genes were upregulated in fibroblasts isolated from tumors compared with normal tissue. Overall, we suggest our protocol as a complementary and more scalable approach compared with single-cell RNA sequencing to investigate associations between clinical parameters and transcriptomic alterations of specific cell populations in esophageal adenocarcinoma

Untersuchung von BSE-Nachkommen auf Protease-resistentes Prion Protein (PrPres) im Blut

Das Ziel der vorliegenden Arbeit war, zu untersuchen, ob im Blut von schweizerischen BSE-Nachkommen (Gruppe A) Protease-resistentes Prion Protein (PrPres) vorkommt und ob sich die Häufigkeit des Vorkommens von derjenigen einer gesunden Kontrollpopulation aus dem Jahr 2006 (Gruppe B) unterscheidet. Die Gruppe A bestand aus 181 Nachkommen von an BSE erkrankten Kühen, die Gruppe B aus 240 gesunden Rindern aus einem Gebiet, in welchem in den Jahren 2001 bis 2006 keine BSE diagnostiziert worden war. Die Blutproben wurden mit einem BSE-Lebendtest (Alicon PrioTrap®) zum Nachweis von Protease-resistentem Prion Protein untersucht. Um abzuklären, ob zwischen der Zeitdifferenz von der Geburt des Nachkommens bis zur Erkrankung der Mutter an BSE eine Beziehung in Bezug auf den Nachweis von PrPres beim Nachkommen bestand, wurde diese Zeitdauer bei jedem Nachkommen errechnet. Bei 29 (16.1 %) von 181 untersuchten BSE-Nachkommen wurde im Blutplasma PrPres nachgewiesen, 152 Tiere waren negativ. Nachkommen, die innerhalb eines Jahres vor dem Auftreten von klinischen Symptomen des Muttertieres geboren worden waren, wiesen im Blut signifikant häufiger PrPres auf als Tiere, bei denen der zeitliche Abstand von der Geburt bis zur Erkrankung mehr als ein Jahr betragen hatte (P < 0.05). In der Kontrollgruppe wurden 10 von 240 Tieren (4.2 %) positiv auf PrPres getestet. Die Untersuchungen haben gezeigt, dass beim Rind im Blut Protease-resistentes Prion Protein nachgewiesen werden kann und dass dieses bei Nachkommen von BSE-Kühen häufiger vorkommt als bei Tieren aus einer gesunden Kontrollpopulation

The Biotrophic Development of Ustilago maydis Studied by RNA-Seq Analysis

The maize smut fungus Ustilago maydis is a model organism for elucidating host colonization strategies of biotrophic fungi. Here, we performed an in depth transcriptional profiling of the entire plant-associated development of U. maydis wild-type strains. In our analysis, we focused on fungal metabolism, nutritional strategies, secreted effectors, and regulatory networks. Secreted proteins were enriched in three distinct expression modules corresponding to stages on the plant surface, establishment of biotrophy, and induction of tumors. These modules are likely the key determinants for U. maydis virulence. With respect to nutrient utilization, we observed that expression of several nutrient transporters was tied to these virulence modules rather than being controlled by nutrient availability. We show that oligopeptide transporters likely involved in nitrogen assimilation are important virulence factors. By measuring the intramodular connectivity of transcription factors, we identified the potential drivers for the virulence modules. While known components of the b-mating type cascade emerged as inducers for the plant surface and biotrophy module, we identified a set of yet uncharacterized transcription factors as likely responsible for expression of the tumor module. We demonstrate a crucial role for leaf tumor formation and effector gene expression for one of these transcription factors

Learn-Morph-Infer: A new way of solving the inverse problem for brain tumor modeling

Current treatment planning of patients diagnosed with a brain tumor, such as glioma, could significantly benefit by accessing the spatial distribution of tumor cell concentration. Existing diagnostic modalities, e.g. magnetic resonance imaging (MRI), contrast sufficiently well areas of high cell density. In gliomas, however, they do not portray areas of low cell concentration, which can often serve as a source for the secondary appearance of the tumor after treatment. To estimate tumor cell densities beyond the visible boundaries of the lesion, numerical simulations of tumor growth could complement imaging information by providing estimates of full spatial distributions of tumor cells. Over recent years a corpus of literature on medical image-based tumor modeling was published. It includes different mathematical formalisms describing the forward tumor growth model. Alongside, various parametric inference schemes were developed to perform an efficient tumor model personalization, i.e. solving the inverse problem. However, the unifying drawback of all existing approaches is the time complexity of the model personalization which prohibits a potential integration of the modeling into clinical settings. In this work, we introduce a deep learning based methodology for inferring the patient-specific spatial distribution of brain tumors from T1Gd and FLAIR MRI medical scans. Coined as Learn-Morph-Infer, the method achieves real-time performance in the order of minutes on widely available hardware and the compute time is stable across tumor models of different complexity, such as reaction–diffusion and reaction–advection–diffusion models. We believe the proposed inverse solution approach not only bridges the way for clinical translation of brain tumor personalization but can also be adopted to other scientific and engineering domains