Adjuvant dendritic cell therapy in stage IIIB/C melanoma: the MIND-DC randomized phase III trial.

Autologous natural dendritic cells (nDCs) treatment can induce tumor-specific immune responses and clinical responses in cancer patients. In this phase III clinical trial (NCT02993315), 148 patients with resected stage IIIB/C melanoma were randomized to adjuvant treatment with nDCs (n = 99) or placebo (n = 49). Active treatment consisted of intranodally injected autologous CD1c+ conventional and plasmacytoid DCs loaded with tumor antigens. The primary endpoint was the 2-year recurrence-free survival (RFS) rate, whereas the secondary endpoints included median RFS, 2-year and median overall survival, adverse event profile, and immunological response The 2-year RFS rate was 36.8% in the nDC treatment group and 46.9% in the control group (p = 0.31). Median RFS was 12.7 months vs 19.9 months, respectively (hazard ratio 1.25; 90% CI: 0.88-1.79; p = 0.29). Median overall survival was not reached in both treatment groups (hazard ratio 1.32; 90% CI: 0.73-2.38; p = 0.44). Grade 3-4 study-related adverse events occurred in 5% and 6% of patients. Functional antigen-specific T cell responses could be detected in 67.1% of patients tested in the nDC treatment group vs 3.8% of patients tested in the control group (p < 0.001). In conclusion, while adjuvant nDC treatment in stage IIIB/C melanoma patients generated specific immune responses and was well tolerated, no benefit in RFS was observed.

Detection of dendritic cell subsets in the tumor microenvironment by multiplex immunohistochemistry.

Dendritic cells (DCs) are essential in antitumor immunity. In humans, three main DC subsets are defined: two types of conventional DCs (cDC1s and cDC2s) and plasmacytoid DCs (pDCs). To study DC subsets in the tumor microenvironment (TME), it is important to correctly identify them in tumor tissues. Tumor-derived DCs are often analyzed in cell suspensions in which spatial information about DCs which can be important to determine their function within the TME is lost. Therefore, we developed the first standardized and optimized multiplex immunohistochemistry panel, simultaneously detecting cDC1s, cDC2s, and pDCs within their tissue context. We report on this panel's development, validation, and quantitative analysis. A multiplex immunohistochemistry panel consisting of CD1c, CD303, X-C motif chemokine receptor 1, CD14, CD19, a tumor marker, and DAPI was established. The ImmuNet machine learning pipeline was trained for the detection of DC subsets. The performance of ImmuNet was compared with conventional cell phenotyping software. Ultimately, frequencies of DC subsets within several tumors were defined. In conclusion, this panel provides a method to study cDC1s, cDC2s, and pDCs in the spatial context of the TME, which supports unraveling their specific roles in antitumor immunity.

Machine learning analysis of the T cell receptor repertoire identifies sequence features of self-reactivity.

The T cell receptor (TCR) determines specificity and affinity for both foreign and self-peptides presented by the major histocompatibility complex (MHC). Although the strength of TCR interactions with self-pMHC impacts T cell function, it has been challenging to identify TCR sequence features that predict T cell fate. To discern patterns distinguishing TCRs from naive CD4+ T cells with low versus high self-reactivity, we used data from 42 mice to train a machine learning (ML) algorithm that identifies population-level differences between TCRß sequence sets. This approach revealed that weakly self-reactive T cell populations were enriched for longer CDR3ß regions and acidic amino acids. We tested our ML predictions of self-reactivity using retrogenic mice with fixed TCRß sequences. Extrapolating our analyses to independent datasets, we predicted high self-reactivity for regulatory T cells and slightly reduced self-reactivity for T cells responding to chronic infections. Our analyses suggest a potential trade-off between TCR repertoire diversity and self-reactivity. A record of this paper's transparent peer review process is included in the supplemental information.

Multiplex Immunohistochemical Analysis of the Spatial Immune Cell Landscape of the Tumor Microenvironment.

The immune cell landscape of the tumor microenvironment potentially contains information for the discovery of prognostic and predictive biomarkers. Multiplex immunohistochemistry is a valuable tool to visualize and identify different types of immune cells in tumor tissues while retaining its spatial information. Here we provide detailed protocols to analyze lymphocyte, myeloid, and dendritic cell populations in tissue sections. Starting from cutting formalin-fixed paraffin-embedded sections, automatic multiplex staining procedures on an automated platform, scanning of the slides on a multispectral imaging microscope, to the analysis of images using an in-house-developed machine learning algorithm ImmuNet. These protocols can be applied to a variety of tumor specimens by simply switching tumor markers to analyze immune cells in different compartments of the sample (tumor versus invasive margin) and apply nearest-neighbor analysis. This analysis is not limited to tumor samples but can also be applied to other (non-)pathogenic tissues. Improvements to the equipment and workflow over the past few years have significantly shortened throughput times, which facilitates the future application of this procedure in the diagnostic setting.

Identifiability of causal effects in test-negative design studies.

Causal directed acyclic graphs (DAGs) are often used to select variables in a regression model to identify causal effects. Outcome-based sampling studies, such as the 'test-negative design' used to assess vaccine effectiveness, present unique challenges that are not addressed by the common back-door criterion. Here we discuss intuitive, graphical approaches to explain why the common back-door criterion cannot be used for identification of population average causal effects with outcome-based sampling studies. We also describe graphical rules that can be used instead in outcome-based sampling studies when the objective is limited to determining if the causal odds ratio is identifiable, and illustrate recent changes to the free online software Dagitty which incorporate these principles.

Flow rate resonance of actively deforming particles.

Lymphoid organs are unusual multicellular tissues: they are densely packed, but the lymphocytes trafficking through them are actively moving. We hypothesize that the intriguing ability of lymphocytes to avoid jamming and clogging is in part attributable to the dynamic shape changes that cells undergo when they move. In this work, we test this hypothesis by investigating an idealized system, namely, the flow of self-propelled, oscillating particles passing through a narrow constriction in two dimensions (2D), using numerical simulations. We found that deformation allows particles with these properties to flow through a narrow constriction in conditions when non-deformable particles would not be able to do so. Such a flowing state requires the amplitude and frequency of oscillations to exceed threshold values. Moreover, a resonance leading to the maximum flow rate was found when the oscillation frequency matched the natural frequency of the particle related to its elastic stiffness. To our knowledge, this phenomenon has not been described previously. Our findings could have important implications for understanding and controlling flow in a variety of systems in addition to lymphoid organs, such as granular flows subjected to vibration.

In silico cancer immunotherapy trials uncover the consequences of therapy-specific response patterns for clinical trial design and outcome.

Late-stage cancer immunotherapy trials often lead to unusual survival curve shapes, like delayed curve separation or a plateauing curve in the treatment arm. It is critical for trial success to anticipate such effects in advance and adjust the design accordingly. Here, we use in silico cancer immunotherapy trials - simulated trials based on three different mathematical models - to assemble virtual patient cohorts undergoing late-stage immunotherapy, chemotherapy, or combination therapies. We find that all three simulation models predict the distinctive survival curve shapes commonly associated with immunotherapies. Considering four aspects of clinical trial design - sample size, endpoint, randomization rate, and interim analyses - we demonstrate how, by simulating various possible scenarios, the robustness of trial design choices can be scrutinized, and possible pitfalls can be identified in advance. We provide readily usable, web-based implementations of our three trial simulation models to facilitate their use by biomedical researchers, doctors, and trialists.

Interpreting T-cell search "strategies" in the light of evolution under constraints.

Two decades of in vivo imaging have revealed how diverse T-cell motion patterns can be. Such recordings have sparked the notion of search "strategies": T cells may have evolved ways to search for antigen efficiently depending on the task at hand. Mathematical models have indeed confirmed that several observed T-cell migration patterns resemble a theoretical optimum; for example, frequent turning, stop-and-go motion, or alternating short and long motile runs have all been interpreted as deliberately tuned behaviours, optimising the cell's chance of finding antigen. But the same behaviours could also arise simply because T cells cannot follow a straight, regular path through the tight spaces they navigate. Even if T cells do follow a theoretically optimal pattern, the question remains: which parts of that pattern have truly been evolved for search, and which merely reflect constraints from the cell's migration machinery and surroundings? We here employ an approach from the field of evolutionary biology to examine how cells might evolve search strategies under realistic constraints. Using a cellular Potts model (CPM), where motion arises from intracellular dynamics interacting with cell shape and a constraining environment, we simulate evolutionary optimization of a simple task: explore as much area as possible. We find that our simulated cells indeed evolve their motility patterns. But the evolved behaviors are not shaped solely by what is functionally optimal; importantly, they also reflect mechanistic constraints. Cells in our model evolve several motility characteristics previously attributed to search optimisation-even though these features are not beneficial for the task given here. Our results stress that search patterns may evolve for other reasons than being "optimal". In part, they may be the inevitable side effects of interactions between cell shape, intracellular dynamics, and the diverse environments T cells face in vivo.

Tumor microenvironment shows an immunological abscopal effect in patients with NSCLC treated with pembrolizumab-radiotherapy combination.

BACKGROUND: Immunotherapy is currently part of the standard of care for patients with advanced-stage non-small cell lung cancer (NSCLC). However, many patients do not respond to this treatment, therefore combination strategies are being explored to increase clinical benefit. The PEMBRO-RT trial combined the therapeutic programmed cell death 1 (PD-1) antibody pembrolizumab with stereotactic body radiation therapy (SBRT) to increase the overall response rate and study the effects on the tumor microenvironment (TME). METHODS: Here, immune infiltrates in the TME of patients included in the PEMBRO-RT trial were investigated. Tumor biopsies of patients treated with pembrolizumab alone or combined with SBRT (a biopsy of the non-irradiated site) at baseline and during treatment were stained with multiplex immunofluorescence for CD3, CD8, CD20, CD103 and FoxP3 for lymphocytes, pan-cytokeratin for tumors, and HLA-ABC expression was determined. RESULTS: The total number of lymphocytes increased significantly after 6 weeks of treatment in the anti-PD-1 group (fold change: 1.87, 95% CI: 1.06 to 3.29) and the anti-PD-1+SBRT group (fold change: 2.29, 95% CI: 1.46 to 3.60). The combination of SBRT and anti-PD-1 induced a 4.87-fold increase (95% CI: 2.45 to 9.68) in CD103+ cytotoxic T-cells 6 weeks on treatment and a 2.56-fold increase (95% CI: 1.03 to 6.36) after anti-PD-1 therapy alone. Responders had a significantly higher number of lymphocytes at baseline than non-responders (fold difference 1.85, 95% CI: 1.04 to 3.29 for anti-PD-1 and fold change 1.93, 95% CI: 1.08 to 3.44 for anti-PD-1+SBRT). CONCLUSION: This explorative study shows that that lymphocyte infiltration in general, instead of the infiltration of a specific lymphocyte subset, is associated with response to therapy in patients with NSCLC.Furthermore, anti-PD-1+SBRT combination therapy induces an immunological abscopal effect in the TME represented by a superior infiltration of cytotoxic T cells as compared with anti-PD-1 monotherapy.

Paired primary and metastatic lesions of patients with ipilimumab-treated melanoma: high variation in lymphocyte infiltration and HLA-ABC expression whereas tumor mutational load is similar and correlates with clinical outcome.

BACKGROUND: Immune checkpoint inhibitors (ICI) can lead to long-term responses in patients with metastatic melanoma. Still many patients with melanoma are intrinsically resistant or acquire secondary resistance. Previous studies have used primary or metastatic tumor tissue for biomarker assessment. Especially in melanoma, metastatic lesions are often present at different anatomical sites such as skin, lymph nodes, and visceral organs. The anatomical site may directly affect the tumor microenvironment (TME). To evaluate the impact of tumor evolution on the TME and on ICI treatment outcome, we directly compared paired primary and metastatic melanoma lesions for tumor mutational burden (TMB), HLA-ABC status, and tumor infiltrating lymphocytes (TILs) of patients that received ipilimumab. METHODS: TMB was analyzed by sequencing primary and metastatic melanoma lesions using the TruSight Oncology 500 assay. Tumor tissues were subjected to multiplex immunohistochemistry to assess HLA-ABC status and for the detection of TIL subsets (B cells, cytotoxic T cells, helper T cells, and regulatory T cells), by using a machine-learning algorithm. RESULTS: While we observed a very good agreement between TMB of matched primary and metastatic melanoma lesions (intraclass coefficient=0.921), such association was absent for HLA-ABC status, TIL density, and subsets thereof. Interestingly, analyses of different metastatic melanoma lesions within a single patient revealed that TIL density and composition agreed remarkably well, rejecting the hypothesis that the TME of different anatomical sites affects TIL infiltration. Similarly, the HLA-ABC status between different metastatic lesions within patients was also comparable. Furthermore, high TMB, of either primary or metastatic melanoma tissue, directly correlated with response to ipilimumab, whereas lymphocyte density or composition did not. Loss of HLA-ABC in the metastatic lesion correlated to a shorter progression-free survival on ipilimumab. CONCLUSIONS: We confirm the link between TMB and HLA-ABC status and the response to ipilimumab-based immunotherapy in melanoma, but no correlation was found for TIL density, neither in primary nor metastatic lesions. Our finding that TMB between paired primary and metastatic melanoma lesions is highly stable, demonstrates its independency of the time point and location of acquisition. TIL and HLA-ABC status in metastatic lesions of different anatomical sites are highly similar within an individual patient.

Pre-existing chromatin accessibility and gene expression differences among naive CD4+ T cells influence effector potential.

CD4+ T cells have a remarkable potential to differentiate into diverse effector lineages following activation. Here, we probe the heterogeneity present among naive CD4+ T cells before encountering their cognate antigen to ask whether their effector potential is modulated by pre-existing transcriptional and chromatin landscape differences. Single-cell RNA sequencing shows that key drivers of variability are genes involved in T cell receptor (TCR) signaling. Using CD5 expression as a readout of the strength of tonic TCR interactions with self-peptide MHC, and sorting on the ends of this self-reactivity spectrum, we find that pre-existing transcriptional differences among naive CD4+ T cells impact follicular helper T (TFH) cell versus non-TFH effector lineage choice. Moreover, our data implicate TCR signal strength during thymic development in establishing differences in naive CD4+ T cell chromatin landscapes that ultimately shape their effector potential.

Cytotoxic T cells are able to efficiently eliminate cancer cells by additive cytotoxicity.

Lethal hit delivery by cytotoxic T lymphocytes (CTL) towards B lymphoma cells occurs as a binary, "yes/no" process. In non-hematologic solid tumors, however, CTL often fail to kill target cells during 1:1 conjugation. Here we describe a mechanism of "additive cytotoxicity" by which time-dependent integration of sublethal damage events, delivered by multiple CTL transiting between individual tumor cells, mediates effective elimination. Reversible sublethal damage includes perforin-dependent membrane pore formation, nuclear envelope rupture and DNA damage. Statistical modeling reveals that 3 serial hits delivered with decay intervals below 50 min discriminate between tumor cell death or survival after recovery. In live melanoma lesions in vivo, sublethal multi-hit delivery is most effective in interstitial tissue where high CTL densities and swarming support frequent serial CTL-tumor cell encounters. This identifies CTL-mediated cytotoxicity by multi-hit delivery as an incremental and tunable process, whereby accelerating damage magnitude and frequency may improve immune efficacy.

Immunophenotyping Reveals Longitudinal Changes in Circulating Immune Cells During Radium-223 Therapy in Patients With Metastatic Castration-Resistant Prostate Cancer.

BACKGROUND: Radium-223 improves overall survival (OS) in men with bone metastatic castration-resistant prostate cancer (mCRPC). While the exact mechanism behind this survival benefit remains unclear, radium-induced immunological mechanisms might contribute to the OS advantage. We performed a comprehensive evaluation of the immunological changes in mCRPC patients by phenotyping the peripheral blood mononuclear cells (PBMCs) during radium-223 therapy. MATERIALS AND METHODS: In this prospective, single-arm, exploratory study, PBMCs of 30 mCRPC patients were collected before, during, and after treatment with radium-223. Lymphocyte and monocyte counts were analyzed to get insight into general immune cell trends. Next, we analyzed changes in T cell subsets, myeloid-derived suppressor cells (MDSCs), and immune checkpoint expression using linear regression models. Per subset, the 6-month change (% of baseline) was determined. Bootstrapped 95% confidence intervals were used to measure the degree of uncertainty of our findings. RESULTS: We observed a substantial decrease in absolute lymphocyte counts (-0.12 * 10^9 cells/L per injection, 95% CI: -0.143 - -0.102). Simultaneously, an increase was observed in the proportion of T cells that expressed costimulatory (ICOS) or inhibitory (TIM-3, PD-L1, and PD-1) checkpoint molecules. Moreover, the fraction of two immunosuppressive subsets - the regulatory T cells and the monocytic MDSCs - increased throughout treatment. These findings were not more pronounced in patients with an alkaline phosphatase response during therapy. CONCLUSION: Immune cell subsets in patients with mCRPC changed during radium-223 therapy, which warrants further research into the possible immunological consequences of these changes.

A tipping point in cancer-immune dynamics leads to divergent immunotherapy responses and hampers biomarker discovery.

BACKGROUND: Predicting treatment response or survival of cancer patients remains challenging in immuno-oncology. Efforts to overcome these challenges focus, among others, on the discovery of new biomarkers. Despite advances in cellular and molecular approaches, only a limited number of candidate biomarkers eventually enter clinical practice. METHODS: A computational modeling approach based on ordinary differential equations was used to simulate the fundamental mechanisms that dictate tumor-immune dynamics and to investigate its implications on responses to immune checkpoint inhibition (ICI) and patient survival. Using in silico biomarker discovery trials, we revealed fundamental principles that explain the diverging success rates of biomarker discovery programs. RESULTS: Our model shows that a tipping point-a sharp state transition between immune control and immune evasion-induces a strongly non-linear relationship between patient survival and both immunological and tumor-related parameters. In patients close to the tipping point, ICI therapy may lead to long-lasting survival benefits, whereas patients far from the tipping point may fail to benefit from these potent treatments. CONCLUSION: These findings have two important implications for clinical oncology. First, the apparent conundrum that ICI induces substantial benefits in some patients yet completely fails in others could be, to a large extent, explained by the presence of a tipping point. Second, predictive biomarkers for immunotherapy should ideally combine both immunological and tumor-related markers, as a patient's distance from the tipping point can typically not be reliably determined from solely one of these. The notion of a tipping point in cancer-immune dynamics helps to devise more accurate strategies to select appropriate treatments for patients with cancer.

Local actin dynamics couple speed and persistence in a cellular Potts model of cell migration.

Cell migration is astoundingly diverse. Molecular signatures, cell-cell interactions, and environmental structures each play their part in shaping cell motion, yielding numerous morphologies and migration modes. Nevertheless, in recent years, a simple unifying law was found to describe cell migration across many different cell types and contexts: faster cells turn less frequently. This universal coupling between speed and persistence (UCSP) was explained by retrograde actin flow from front to back, but it remains unclear how this mechanism generalizes to cells with complex shapes and cells migrating in structured environments, which may not have a well-defined front-to-back orientation. Here, we present an in-depth characterization of an existing cellular Potts model, in which cells polarize dynamically from a combination of local actin dynamics (stimulating protrusions) and global membrane tension along the perimeter (inhibiting protrusions). We first show that the UCSP emerges spontaneously in this model through a cross talk of intracellular mechanisms, cell shape, and environmental constraints, resembling the dynamic nature of cell migration in vivo. Importantly, we find that local protrusion dynamics suffice to reproduce the UCSP-even in cases in which no clear global, front-to-back polarity exists. We then harness the spatial nature of the cellular Potts model to show how cell shape dynamics limit both the speed and persistence a cell can reach and how a rigid environment such as the skin can restrict cell motility even further. Our results broaden the range of potential mechanisms underlying the speed-persistence coupling that has emerged as a fundamental property of migrating cells.

Artistoo, a library to build, share, and explore simulations of cells and tissues in the web browser.

The cellular Potts model (CPM) is a powerful in silico method for simulating biological processes at tissue scale. Their inherently graphical nature makes CPMs very accessible in theory, but in practice, they are mostly implemented in specialised frameworks users need to master before they can run simulations. We here present Artistoo (Artificial Tissue Toolbox), a JavaScript library for building 'explorable' CPM simulations where viewers can change parameters interactively, exploring their effects in real time. Simulations run directly in the web browser and do not require third-party software, plugins, or back-end servers. The JavaScript implementation imposes no major performance loss compared to frameworks written in C++; Artistoo remains sufficiently fast for interactive, real-time simulations. Artistoo provides an opportunity to unlock CPM models for a broader audience: interactive simulations can be shared via a URL in a zero-install setting. We discuss applications in CPM research, science dissemination, open science, and education.

Understanding complex systems, such as the weather or the spread of a pandemic, often relies on computational models that can simulate what is happening and what will happen next. Models like these can also be used to investigate biological processes. For example, cellular Potts models (or CPMs for short) are regularly used to simulate how cells move, self-organise to form tissues and respond to their surroundings. Computational biologists use a range of specialist skills and software to create these models. However, this can make it difficult for people who do not have programming experience to interact with these simulations and incorporate them in to their own research. If more people could engage with these models, this could help foster closer collaborations and ultimately lead to better models of biological systems. To make CPMs more accessible, Wortel and Textor created a toolbox called Artistoo that allows users to view and interact with simulations using just an internet browser. These simulations are very easy to interact with, and do not require any prior programming knowledge or specialised software. Viewers can input different parameters in to the simulation and watch in real time to see how this affects the biological system being modelled. Wortel and Textor showed that this toolbox can be used to build a range of different biological models and works just as fast as other, more complex programming tools. Artistoo has many potential applications and is a valuable education, learning, and collaboration tool. It may also encourage more open science, as having more accessible computational models could help with peer review and make it easier to collaborate across different research fields. A similar approach could be used to provide access to many other types of models in biology and beyond.

Human type 1 and type 2 conventional dendritic cells express indoleamine 2,3-dioxygenase 1 with functional effects on T cell priming.

Dendritic cells (DCs) are key regulators of the immune system that shape T cell responses. Regulation of T cell induction by DCs may occur via the intracellular enzyme indoleamine 2,3-dioxygenase 1 (IDO), which catalyzes conversion of the essential amino acid tryptophan into kynurenine. Here, we examined the role of IDO in human peripheral blood plasmacytoid DCs (pDCs), and type 1 and type 2 conventional DCs (cDC1s and cDC2s). Our data demonstrate that under homeostatic conditions, IDO is selectively expressed by cDC1s. IFN-γ or TLR ligation further increases IDO expression in cDC1s and induces modest expression of the enzyme in cDC2s, but not pDCs. IDO expressed by conventional DCs is functionally active as measured by kynurenine production. Furthermore, IDO activity in TLR-stimulated cDC1s and cDC2s inhibits T cell proliferation in settings were DC-T cell cell-cell contact does not play a role. Selective inhibition of IDO1 with epacadostat, an inhibitor currently tested in clinical trials, rescued T cell proliferation without affecting DC maturation status or their ability to cross-present soluble antigen. Our findings provide new insights into the functional specialization of human blood DC subsets and suggest a possible synergistic enhancement of therapeutic efficacy by combining DC-based cancer vaccines with IDO inhibition.

Testing Graphical Causal Models Using the R Package "dagitty".

Causal diagrams such as directed acyclic graphs (DAGs) are used in several scientific fields to help design and analyze studies that aim to infer causal effects from observational data; for example, DAGs can help identify suitable strategies to reduce confounding bias. However, DAGs can be difficult to design, and the validity of any DAG-derived strategy hinges on the validity of the postulated DAG itself. Researchers should therefore check whether the assumptions encoded in the DAG are consistent with the data before proceeding with the analysis. Here, we explain how the R package 'dagitty', based on the web tool dagitty.net, can be used to test the statistical implications of the assumptions encoded in a given DAG. We hope that this will help researchers discover model specification errors, avoid erroneous conclusions, and build better models. © 2021 The Authors. Basic Protocol 1: Constructing and importing DAG models from the dagitty web interface Support Protocol 1: Installing R, RStudio, and the dagitty package Basic Protocol 2: Testing DAGs against categorical data Basic Protocol 3: Testing DAGs against continuous data Support Protocol 2: Testing DAGs against continuous data with non-linearities Basic Protocol 4: Testing DAGs against a combination of categorical and continuous data.

CelltrackR: An R package for fast and flexible analysis of immune cell migration data.

Visualization of cell migration via time-lapse microscopy has greatly advanced our understanding of the immune system. However, subtle differences in migration dynamics are easily obscured by biases and imaging artifacts. While several analysis methods have been suggested to address these issues, an integrated tool implementing them is currently lacking. Here, we present celltrackR, an R package containing a diverse set of state-of-the-art analysis methods for (immune) cell tracks. CelltrackR supports the complete pipeline for track analysis by providing methods for data management, quality control, extracting and visualizing migration statistics, clustering tracks, and simulating cell migration. CelltrackR supports the analysis of both 2D and 3D cell tracks. CelltrackR is an open-source package released under the GPL-2 license, and is freely available on both GitHub and CRAN. Although the package was designed specifically for immune cell migration data, many of its methods will also be of use in other research areas dealing with moving objects.