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

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

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

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

P2Y6 receptor signaling in natural killer cells impairs insulin sensitivity in obesity

Natural killer (NK) cells contribute to the development of obesity-associated insulin resistance and have previously been shown to up-regulate the expression of the P2Y purinoreceptor 6 (P2Y6R) upon high fat diet (HFD)-induced obesity. Here, we reveal that NK cell-specific inactivation of the P2Y6R gene improves insulin sensitivity in obese mice and reduces the expression of chemokines in adipose tissue infiltrating NK-cells. Obese mice lacking P2Y6R specifically in NK cells exhibited a reduction in adipose tissue inflammation, exhibited improved insulin-stimulated suppression of lipolysis in adipose tissue and a reduction in hepatic glucose production, leading to an overall improvement of systemic insulin sensitivity. In contrast, myeloid lineage specific P2Y6R inactivation does not affect energy or glucose homeostasis in obesity. Collectively, we show that P2Y6R signaling in NK cells contributes to the development of obesity-associated insulin resistance and thus might be a future target for the treatment of obesity-associated insulin resistance and type 2 diabetes.Competing Interest StatementThe authors have declared no competing interest.µCTMicro computer tomographyAgRPAgouti-related proteinAKTProtein kinase BApoa5Apolipoprotein A-VATPAdenosine triphosphateBATBrown adipose tissueBMDMBone marrow-derived macrophagesCcl3Chemokine (C-C motif) ligand 3Ccl4Chemokine (C-C motif) ligand 4CDControl DietCLSCrown-like structurescNKconventional NK cellsCxcl16Chemokine (C-X-C motif) ligand 16DAGDiacylglyceridesDNDouble negativeECARExtracellular acidification rateOCROxygen consumption rateELISAEnzyme-linked immunosorbent assayERKExtracellular signal-regulated kinasesFFAFree fatty acidsGIRGlucose infusion rateGTTGlucose tolerance testHFDHigh fat dietHGPHepatic glucose productionHOMA-IRHomeostasis model assessment- insulin resistanceHSLHormone-sensitive lipaseIFNγInterferon gammaILInterleukinILCInnate lymphoid cellITTInsulin tolerance testLilra5Leukocyte immunoglobulin like receptor A5LplLipoprotein lipaseMAPKMitogen-activated protein kinaseNCDNormal chow dietNK cellNatural killer cellOCROxygen consumption rateOXPHOSOxidative phosphorylationP2Y6RP2Y purinoreceptor 6PGATPerigonodal adipose tissuepHSLphosphorylated hormone-sensitive lipasePI3KPhosphoinositide 3-kinasePKCProtein kinase CPlin2Perilipin 2RERRespiratory exchange rateRoraRAR-related orphan receptor alphaSEMStandard error of the meanSKMSkeletal muscleSVFStromal vascular fractionTGTriglyceridesTNFαTumor necrosis factor alphaTPMTranscripts per milliontrNKtissue-resident NK cellsUDPUridine diphosphateuNKuterine NK cellsXcl1Chemokine (C motif) ligan

Noncanonical function of AGO2 augments T-cell receptor signaling in T-cell prolymphocytic leukemia

T-cell prolymphocytic leukemia (T-PLL) is a chemotherapy-refractory T-cell malignancy with limited therapeutic options and a poor prognosis. Current disease concepts implicate TCL1A oncogene-mediated enhanced T-cell receptor (TCR) signaling and aberrant DNA repair as central perturbed pathways. We discovered that recurrent gains on chromosome 8q more frequently involve the AGO2 gene than the adjacent MYC locus as the affected minimally amplified genomic region. AGO2 has been understood as a pro-tumorigenic key regulator of microRNA (miR) processing. In primary tumor material and cell line models, AGO2 overrepresentation associated (i) with higher disease burden, (ii) with enhanced in vitro viability and growth of leukemic T-cells, and (iii) with miR-omes and transcriptomes that highlight altered survival signaling, abrogated cell cycle control, and defective DNA damage responses. Moreover, AGO2 elicited immediate, rather than non-RNA mediated, effects in leukemic T-cells. Systems of genetically modulated AGO2 revealed that it enhances TCR signaling, particularly at the level of ZAP70, PLCγ1, and LAT kinase phospho-activation. In global mass-spectrometric analyses, AGO2 interacted with a unique set of partners in a TCR-stimulated context, including the TCR kinases LCK and ZAP70, forming membranous protein complexes. Models of their three-dimensional structure also suggested that AGO2 undergoes post-transcriptional modi-fications by LCK. This novel TCR-associated non-canonical function of AGO2 represents, in addition to TCL1A-mediated TCR signal augmentation, another enhancer mechanism of this important deregulated growth pathway in T-PLL. These findings further emphasize TCR signaling intermediates as candidates for therapeutic targeting