Johannes Textor

DAGitty: a graphical tool for analyzing causal diagrams.

Johannes Textor, Maciej Liskiewicz Identifying and controlling bias is a key problem in empirical sciences. Causal diagram theory provides graphical criteria for deciding whether and how causal effects can be identified from observed (nonexperimental) data by covariate adjustment. Here we prove equivalences between existing as well as new criteria for adjustment and we provide a new simplified but still equivalent notion of d-separation. These lead to efficient algorithms for two important tasks in causal diagram analysis: (1) listing minimal covariate adjustments (with polynomial delay); and (2) identifying the subdiagram involved in biasing paths (in linear time). Our results improve upon existing exponential-time solutions for these problems, enabling users to assess the effects of covariate adjustment on diagrams with tens to hundreds of variables interactively in real time.

Defining the quantitative limits of intravital two-photon lymphocyte tracking

Two-photon microscopy has substantially advanced our understanding of cellular dynamics in the immune system. Cell migration can now be imaged in real time in the living animal. Strikingly, the migration of naive lymphocytes in secondary lymphoid tissue appears predominantly random. It is unclear, however, whether directed migration may escape detection in this random background. Using a combination of mathematical modeling and experimental data, we investigate the extent to which modern two-photon imaging can rule out biologically relevant directed migration. For naive T cells migrating in uninfected lymph nodes (LNs) at average 3D speeds of around 18 μm/min, we rule out uniform directed migration of more than 1.7 μm/min at the 95% confidence level, confirming that T cell migration is indeed mostly random on a timescale of minutes. To investigate whether this finding still holds for longer timescales, we use a 3D simulation of the naive T cell LN transit. A pure random walk predicts a transit time of around 16 h, which is in good agreement with experimental results. A directional bias of only 0.5 μm/min—less than 3% of the cell speed—would already accelerate the transit twofold. These results jointly strengthen the random walk analogy for naive T cell migration in LNs, but they also emphasize that very small deviations from random migration can still be important. Our methods are applicable to cells of any type and can be used to reanalyze existing datasets.

Age-dependent cell trafficking defects in draining lymph nodes impair adaptive immunity and control of West Nile virus infection

Impaired immune responses in the elderly lead to reduced vaccine efficacy and increased susceptibility to viral infections. Although several groups have documented age-dependent defects in adaptive immune priming, the deficits that occur prior to antigen encounter remain largely unexplored. Herein, we identify novel mechanisms for compromised adaptive immunity that occurs with aging in the context of infection with West Nile virus (WNV), an encephalitic flavivirus that preferentially causes disease in the elderly. An impaired IgM and IgG response and enhanced vulnerability to WNV infection during aging was linked to delayed germinal center formation in the draining lymph node (DLN). Adoptive transfer studies and two-photon intravital microscopy revealed a decreased trafficking capacity of donor naïve CD4+ T cells from old mice, which manifested as impaired T cell diapedesis at high endothelial venules and reduced cell motility within DLN prior to antigen encounter. Furthermore, leukocyte accumulation in the DLN within the first few days of WNV infection or antigen-adjuvant administration was diminished more generally in old mice and associated with a second aging-related defect in local cytokine and chemokine production. Thus, age-dependent cell-intrinsic and environmental defects in the DLN result in delayed immune cell recruitment and antigen recognition. These deficits compromise priming of early adaptive immune responses and likely contribute to the susceptibility of old animals to acute WNV infection.

Random migration and signal integration promote rapid and robust T cell recruitment.

To fight infections, rare T cells must quickly home to appropriate lymph nodes (LNs), and reliably localize the antigen (Ag) within them. The first challenge calls for rapid trafficking between LNs, whereas the second may require extensive search within each LN. Here we combine simulations and experimental data to investigate which features of random T cell migration within and between LNs allow meeting these two conflicting demands. Our model indicates that integrating signals from multiple random encounters with Ag-presenting cells permits reliable detection of even low-dose Ag, and predicts a kinetic feature of cognate T cell arrest in LNs that we confirm using intravital two-photon data. Furthermore, we obtain the most reliable retention if T cells transit through LNs stochastically, which may explain the long and widely distributed LN dwell times observed in vivo. Finally, we demonstrate that random migration, both between and within LNs, allows recruiting the majority of cognate precursors within a few days for various realistic infection scenarios. Thus, the combination of two-scale stochastic migration and signal integration is an efficient and robust strategy for T cell immune surveillance.

T-cell Landscape in a Primary Melanoma Predicts the Survival of Patients with Metastatic Disease after Their Treatment with Dendritic Cell Vaccines

Tumor-infiltrating lymphocytes appear to be a predictor of survival in many cancers, including cutaneous melanoma. We applied automated multispectral imaging to determine whether density and distribution of T cells within primary cutaneous melanoma tissue correlate with survival of metastatic melanoma patients after dendritic cell (DC) vaccination. CD3(+) T cell infiltration in primary tumors from 77 metastatic melanoma patients was quantified using the ratio of intratumoral versus peritumoral T-cell densities (I/P ratio). Patients with longer survival after DC vaccination had stronger T-cell infiltration than patients with shorter survival in a discovery cohort of 19 patients (P = 0.000026) and a validation cohort of 39 patients (P = 0.000016). I/P ratio was the strongest predictor of survival in a multivariate analysis including M substage and serum lactate dehydrogenase level. To evaluate I/P ratio as a predictive biomarker, we analyzed 19 chemotherapy-treated patients. Longer survival times of DC-vaccinated compared with chemotherapy-treated patients was observed for high (P = 0.000566), but not low (P = 0.154) I/P ratios. In conclusion, T-cell infiltration into primary melanoma is a strong predictor of survival after DC vaccination in metastatic melanoma patients who, on average, started this therapy several years after primary tumor resection. The infiltration remains predictive even after adjustment for late-stage prognostic markers. Our findings suggest that the I/P ratio is a potential predictive biomarker for treatment selection. Cancer Res; 76(12); 3496-506. ©2016 AACR.

Efficient Algorithms for String-Based Negative Selection

String-based negative selection is an immune-inspired classification scheme: Given a self-set S of strings, generate a set D of detectors that do not match any element of S . Then, use these detectors to partition a monitor set M into self and non-self elements. Implementations of this scheme are often impractical because they need exponential time in the size of S to construct D . Here, we consider r -chunk and r -contiguous detectors, two common implementations that suffer from this problem, and show that compressed representations of D are constructible in polynomial time for any given S and r . Since these representations can themselves be used to classify the elements in M , the worst-case running time of r -chunk and r -contiguous detector based negative selection is reduced from exponential to polynomial.

Negative selection algorithms without generating detectors

Negative selection algorithms are immune-inspired classifiers that are trained on negative examples only. Classification is performed by generating detectors that match none of the negative examples, and these detectors are then matched against the elements to be classified. This can be a performance bottleneck: A large number of detectors may be required for acceptable sensitivity, or finding detectors that match none of the negative examples may be difficult. In this paper, we show how negative selection can be implemented without generating detectors explicitly, which for many detector types leads to polynomial time algorithms whereas the common approach to sample detectors randomly takes exponential time in the worst case. In particular, we show that negative selection on strings with generating all detectors can be efficiently simulated without detectors if, and only if, an associated decision problem can be answered efficiently, regardless the detector type. We also show how to efficiently simulate the more general case in which only a limited number of detectors is generated. For many detector types this non-exhaustive negative selection is more meaningful but it can be computationally more difficult, which we illustrate using Boolean monomials.

Crawling and Gliding: A Computational Model for Shape-Driven Cell Migration.

Cell migration is a complex process involving many intracellular and extracellular factors, with different cell types adopting sometimes strikingly different morphologies. Modeling realistically behaving cells in tissues is computationally challenging because it implies dealing with multiple levels of complexity. We extend the Cellular Potts Model with an actin-inspired feedback mechanism that allows small stochastic cell rufflings to expand to cell protrusions. This simple phenomenological model produces realistically crawling and deforming amoeboid cells, and gliding half-moon shaped keratocyte-like cells. Both cell types can migrate randomly or follow directional cues. They can squeeze in between other cells in densely populated environments or migrate collectively. The model is computationally light, which allows the study of large, dense and heterogeneous tissues containing cells with realistic shapes and migratory properties.

Eight-Color Multiplex Immunohistochemistry for Simultaneous Detection of Multiple Immune Checkpoint Molecules within the Tumor Microenvironment

Therapies targeting immune checkpoint molecules CTLA-4 and PD-1/PD-L1 have advanced the field of cancer immunotherapy. New mAbs targeting different immune checkpoint molecules, such as TIM3, CD27, and OX40, are being developed and tested in clinical trials. To make educated decisions and design new combination treatment strategies, it is vital to learn more about coexpression of both inhibitory and stimulatory immune checkpoints on individual cells within the tumor microenvironment. Recent advances in multiple immunolabeling and multispectral imaging have enabled simultaneous analysis of more than three markers within a single formalin-fixed paraffin-embedded tissue section, with accurate cell discrimination and spatial information. However, multiplex immunohistochemistry with a maximized number of markers presents multiple difficulties. These include the primary Ab concentrations and order within the multiplex panel, which are of major importance for the staining result. In this article, we report on the development, optimization, and application of an eight-color multiplex immunohistochemistry panel, consisting of PD-1, PD-L1, OX40, CD27, TIM3, CD3, a tumor marker, and DAPI. This multiplex panel allows for simultaneous quantification of five different immune checkpoint molecules on individual cells within different tumor types. This analysis revealed major differences in the immune checkpoint expression patterns across tumor types and individual tumor samples. This method could ultimately, by characterizing the tumor microenvironment of patients who have been treated with different immune checkpoint modulators, form the rationale for the design of immune checkpoint-based immunotherapy in the future.

Adjuvant dendritic cell vaccination induces tumor-specific immune responses in the majority of stage III melanoma patients.

ABSTRACT Purpose: To determine the effectiveness of adjuvant dendritic cell (DC) vaccination to induce tumor-specific immunological responses in stage III melanoma patients. Experimental design: Retrospective analysis of stage III melanoma patients, vaccinated with autologous monocyte-derived DC loaded with tumor-associated antigens (TAA) gp100 and tyrosinase after radical lymph node dissection. Skin-test infiltrating lymphocytes (SKILs) obtained from delayed-type hypersensitivity skin-test biopsies were analyzed for the presence of TAA-specific CD8+ T cells by tetrameric MHC-peptide complexes and by functional TAA-specific T cell assays, defined by peptide-recognition (T2 cells) and/or tumor-recognition (BLM and/or MEL624) with specific production of Th1 cytokines and no Th2 cytokines. Results: Ninety-seven patients were analyzed: 21 with stage IIIA, 34 with stage IIIB, and 42 had stage IIIC disease. Tetramer-positive CD8+ T cells were present in 68 patients (70%), and 24 of them showed a response against all 3 epitopes tested (gp100:154–162, gp100:280–288, and tyrosinase:369–377) at any point during vaccinations. A functional T cell response was found in 62 patients (64%). Rates of peptide-recognition of gp100:154–162, gp100:280–288, and tyrosinase:369–377 were 40%, 29%, and 45%, respectively. Median recurrence-free survival and distant metastasis-free survival of the whole study population were 23.0 mo and 36.8 mo, respectively. Conclusions: DC vaccination induces a functional TAA-specific T cell response in the majority of stage III melanoma patients, indicating it is more effective in stage III than in stage IV melanoma patients. Furthermore, performing multiple cycles of vaccinations enhances the chance of a broader immune response.