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Background Neoantigens are tumor-specific antigens that are important in the anti-tumor immune response. These antigens are not subject to central immune tolerance and are therefore potentially more immunogenic than tumor-associated antigens. NEO-STIM®, our propriety ex vivo induction process, was developed to generate T-cell products specific to these neoantigens from the peripheral blood of patient. Here, we present the results of a proof of concept, pre-clinical study with multiple successful process engineering runs generating a neoantigen-specific T-cell product (NEO-PTC-01) using leukaphereses from metastatic melanoma patients. These products contain specific T-cell responses targeting multiple neoantigens from each individual patient‘s tumor. Methods Patient-specific neoantigens were predicted using our RECON® bioinformatics platform. Predicted high-quality neoantigens were utilized in our ex vivo stimulation protocol, NEO-STIM, in the process engineering runs of NEO-PTC-01. NEO-STIM is used to prime, activate and expand memory and de novo T-cell responses from both the CD4+ and the CD8+ compartment. High throughput flow cytometric analysis was performed to characterize the specificity and functionality (cytokine production and cytolytic capacity) of the induced T-cell responses. Results Here we present the successful induction of 4–5 CD8+ and 4–7 CD4+ T-cell responses per patient, generated using peripheral blood mononuclear cells from multiple melanoma patients during these successful process engineering runs. We then extensively characterized these T-cell responses and demonstrate that these responses are functional, specific and have cytolytic capacity. Moreover, the induced T cells can recognize autologous tumor. Conclusions NEO-STIM is a novel platform that generates ex vivo T-cell responses to high-quality neoantigen targets. NEO-PTC-01, the neoantigen-specific T cell product generated from this process, is a potent adoptive cell therapy targeting multiple immunogenic neoantigens in patients with metastatic melanoma.

The activation induced cytidine deaminase/apolipoprotein B editing complex (AID/APOBEC) family comprises several nucleic acid editors with roles ranging from antibody diversification to mRNA editing. APOBEC2, an evolutionarily conserved member of this family, has neither an established substrate nor a mechanism of action, however genetic evidence suggests functional relevance in tissues such as muscle. Here, we demonstrate that in muscle, APOBEC2 does not have any of the attributed molecular functions of the AID/APOBEC family, such as RNA editing, DNA demethylation, or DNA mutation. Instead, we show that APOBEC2 occupies chromatin at promoter regions of certain genes, whose expression is repressed during muscle cell differentiation. We further demonstrate that APOBEC2 on one hand binds promoter region DNA directly and in a sequence specific fashion, while on the other it interacts with HDAC transcriptional corepressor complexes. Therefore, APOBEC2, by actively repressing the expression of non-myogenesis pathway genes, plays a key role in enforcing the proper establishment of muscle cell fate.

BackgroundThe ongoing COVID-19 pandemic has created an urgency to identify novel vaccine targets for protective immunity against SARS-CoV-2. Consistent with observations for SARS-CoV, a closely related coronavirus responsible for the 2003 SARS outbreak, early reports identify a protective role for both humoral and cell-mediated immunity for SARS CoV-2.MethodsIn this study, we leveraged HLA-I and HLA-II T cell epitope prediction tools from RECON® (Real-time Epitope Computation for ONcology), our bioinformatic pipeline that was developed using proteomic profiling of individual HLA-I and HLA-II alleles to predict rules for peptide binding to a diverse set of such alleles. We applied these binding predictors to viral genomes from the Coronaviridae family, and specifically to identify SARS-CoV-2 T cell epitopes.ResultsTo test the suitability of these tools to identify viral T cell epitopes, we first validated HLA-I and HLA-II predictions on Coronaviridae family epitopes deposited in the Virus Pathogen Database and Analysis Resource (ViPR) database. We then use our HLA-I and HLA-II predictors to identify 11,776 HLA-I and 7,991 HLA-II candidate binding peptides across all 12 open reading frames (ORFs) of SARS-CoV-2. This extensive list of identified candidate peptides is driven by the length of the ORFs and the significant number of HLA-I and HLA-II alleles that we are able to predict (74 and 83, respectively), providing over 99% coverage for the US, European and Asian populations, for both HLA-I and HLA-II. From our SARS-CoV-2 predicted peptide-HLA-I allele pairs, 368 pairs identically matched previously reported pairs in the ViPR database, originating from other forms of coronaviruses. 320 of these pairs (89.1%) had a positive MHC-binding assay result. This analysis reinforces the validity our predictions.ConclusionsUsing this bioinformatic platform, we identify multiple putative epitopes for CD4+ and CD8+ T cells whose HLA binding properties cover nearly the entire population and thus may be effective when included in prophylactic vaccines against SARS-CoV-2 to induce broad cellular immunity.

Summary Neoantigens arise from mutations in cancer cells and are important targets of T cell-mediated anti-tumor immunity. Here, we report the first open-label, phase Ib clinical trial of a personalized neoantigen-based vaccine, NEO-PV-01, in combination with PD-1 blockade in patients with advanced melanoma, non-small cell lung cancer, or bladder cancer. This analysis of 82 patients demonstrated that the regimen was safe, with no treatment-related serious adverse events observed. De novo neoantigen-specific CD4+ and CD8+ T cell responses were observed post-vaccination in all of the patients. The vaccine-induced T cells had a cytotoxic phenotype and were capable of trafficking to the tumor and mediating cell killing. In addition, epitope spread to neoantigens not included in the vaccine was detected post-vaccination. These data support the safety and immunogenicity of this regimen in patients with advanced solid tumors (Clinicaltrials.gov: NCT02897765).

BackgroundNeoantigens are tumor-specific antigens that are important in the anti-tumor immune response. These antigens are not subject to central immune tolerance and are therefore potentially more immunogenic than tumor-associated antigens. Here, we present the results of a proof-of-concept, pre-clinical study with multiple successful patient material runs generating a neoantigen-specific T-cell product (BNT221/NEO-PTC-01) using leukaphereses from patients with ovarian cancer. These products contain specific T-cell responses targeting multiple neoantigens from each individual patient‘s tumor.MethodsLeukapheresis and tumor biopsy samples were obtained from multiple patients with ovarian cancer and metastatic melanoma cancer under IRB approval using the N16NEON protocol at the Netherlands Cancer Institute subsidized by BioNTech US. Patient-specific neoantigens from the patient‘s biopsy were predicted using our RECON® bioinformatics platform and the best scoring neoantigens were encoded into synthetic peptides or mRNA molecules and were utilized in our ex vivo stimulation protocol, NEO-STIM®, which is used to prime, activate, and expand memory and de novo T-cell responses from both the CD4+ and the CD8+ compartment. High-throughput flow cytometric analysis was performed to characterize the specificity and functionality (cytokine production and cytolytic capacity) of the induced T-cell responses.ResultsNEO-STIM generates T-cell products specific to neoantigens from the peripheral blood of patients. Data will be presented showing the successful induction of 2–10 CD8+ and 4–13 CD4+ T-cell responses per patient, generated using peripheral blood mononuclear cells from patients with ovarian cancer using our NEO-STIM platform. We extensively characterized these T-cell responses and demonstrate that these responses are polyfunctional, specific and have the capacity to degranulate. T cells in the induced product are of effector memory and central memory phenotypes.ConclusionsNEO-STIM is a novel platform that generates ex vivo T-cell responses to high-quality neoantigen targets. Efforts are ongoing to upscale the manufacturing process and move this into a phase I study. BNT221, the neoantigen-specific T cell product generated from this process, is a potent adoptive cell therapy targeting multiple immunogenic neoantigens in patients with metastatic ovarian cancer.Ethics ApprovalLeukapheresis and tumor biopsy samples were obtained from multiple patients with ovarian cancer and metastatic melanoma cancer under IRB approval using the N16NEON protocol at the Netherlands Cancer Institute subsidized by BioNTech US.

Microglia (MG), a heterogeneous population of phagocytic cells, play important roles in central nervous system (CNS) homeostasis and neural plasticity. Under steady-state conditions, MG maintain homeostasis by producing antiinflammatory cytokines and neurotrophic factors, support myelin production, and remove synapses and cellular debris, as well as participating in "cross-correction," a process that supplies neurons with key factors for executing autophagy-lysosomal function. As sentinels for the immune system, MG also detect "danger" signals (pathogenic or traumatic insult), become activated, produce proinflammatory cytokines, and recruit monocytes and dendritic cells to the site of damage through a breached blood-brain barrier or via brain lymphatics. Failure to effectively resolve MG activation can be problematic and can lead to chronic inflammation, a condition proposed to underlie CNS pathophysiology in heritable brain disorders and age-related neurodegenerative and cognitive decline. Here, we show that APOBEC1-mediated RNA editing occurs within MG and is key to maintaining their resting status. Like bone marrow-derived macrophages, RNA editing in MG leads to overall changes in the abundance of edited proteins that coordinate the function of multiple cellular pathways. Conversely, mice lacking the APOBEC1 editing function in MG display evidence of dysregulation, with progressive age-related signs of neurodegeneration, characterized by clustering of activated MG, aberrant myelination, increased inflammation, and lysosomal anomalies that culminate in behavioral and motor deficiencies. Collectively, our study identifies posttranscriptional modification by RNA editing as a critical regulatory mechanism of vital cellular functions that maintain overall brain health.

Summary Increasing evidence indicates CD4+ T cells can recognize cancer-specific antigens and control tumor growth. However, it remains difficult to predict the antigens that will be presented by human leukocyte antigen class II molecules (HLA-II), hindering efforts to optimally target them therapeutically. Obstacles include inaccurate peptide-binding prediction and unsolved complexities of the HLA-II pathway. To address these challenges, we developed an improved technology for discovering HLA-II binding motifs and conducted a comprehensive analysis of tumor ligandomes to learn processing rules relevant in the tumor microenvironment. We profiled >40 HLA-II alleles and showed that binding motifs were highly sensitive to HLA-DM, a peptide-loading chaperone. We also revealed that intratumoral HLA-II presentation was dominated by professional antigen-presenting cells (APCs) rather than cancer cells. Integrating these observations, we developed algorithms that accurately predicted APC ligandomes, including peptides from phagocytosed cancer cells. These tools and biological insights will enable improved HLA-II-directed cancer therapies.

The ongoing COVID-19 pandemic has created an urgency to identify novel vaccine targets for protective immunity against SARS-CoV-2. Consistent with observations for the closely related SARS-CoV, early reports identify a protective role for both humoral and cell-mediated immunity for SARS-CoV-2. In this study, we leveraged our bioinformatics binding prediction tools for human leukocyte antigen (HLA)-I and HLA-II alleles that cover nearly the entire population and were developed using mass spectrometry-based profiling of 74 individual HLA-I and 83 individual HLA-II alleles. We applied these binding predictors, initially developed to predict tumor neoantigen presentation, to identify T-cell epitopes from SARS-CoV-2 proteins. To determine the ability of our tools to identify viral T-cell epitopes, we validated HLA-I and HLA-II predictions on Coronaviridae family epitopes deposited in the Virus Pathogen Database and Analysis Resource (ViPR) database. We then applied our HLA-I and HLA-II predictors to 13 open reading frames (ORFs) of SARS-CoV-2 and identified 11,897 HLA-I and 8,046 HLA-II candidate peptides that were highly ranked for binding. From our SARS-CoV-2 predicted peptide-HLA-I allele pairs, 374 pairs identically matched previously reported pairs in the ViPR database, originating from other coronaviruses with homologous sequences. Of these pairs, 333 (89 %) had a positive HLA-binding assay result, reinforcing the validity of our predictions. Furthermore, we assayed a subset of epitopes with highly predicted binding scores for their ability to be recognized by specific CD8+ T cell in human donor PBMCs. These epitopes were chosen from four structural proteins (S, N, M, E) and one nonstructural protein (ORF1ab) from SARS-CoV-2, and epitopes from all five proteins were found to be immunogenic. Finally, it was important to address the expression of SARS-CoV-2 proteins within cells since their subsequent processing is necessary for MHC presentation and the generation of specific epitopes. We utilized publicly available proteomic data to infer the relative expression of SARS-CoV-2 proteins from infected cell lines and determined that the different proteins vary significantly in their expression levels, with the nucleocapsid being the most highly expressed viral protein across these studies. Our predictions identify few epitopes from each SARS-CoV-2 protein, which are predicted to bind multiple HLA-I or HLA-II alleles, potentially covering over 99% of the USA, European, and Asian populations. Finally, using our bioinformatic platform, we identify multiple putative epitopes that are potential targets for CD4+ and CD8+ T cells whose predicted HLA binding properties cover nearly the entire population. We further propose that when considering the protein expression levels of these epitopes and their ability to elicit a T-cell response, these epitopes may be effective when included in vaccines against SARS-CoV-2 to induce broad cellular immunity. Citation Format: Asaf Poran, Dewi Harjanto, Matt Malloy, Christina M. Arieta, Daniel A. Rothenberg, Divya Lenkala, Marit M. van Buuren, Terri A. Addona, Michael S. Rooney, Lakshmi Srinivasan, Richard B. Gaynor. Sequence-based prediction of SARS-CoV-2 vaccine targets using a mass spectrometry-based bioinformatics predictor identifies immunogenic T-cell epitopes [abstract]. In: Proceedings of the AACR Virtual Meeting: COVID-19 and Cancer; 2020 Jul 20-22. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(18_Suppl):Abstract nr S03-02.

While tumor-reactive CD4+ T cells have been associated with effective immunotherapy responses, accurate prediction of MHC class II-restricted ligands remains a challenge, limiting our ability to harness CD4+ immunity for cancer therapy. To this end, we have developed a state-of-the-art MHC class II binding predictor, neonmhc2, trained on HLA-II ligands identified from mass spectrometry (MS) of cell lines engineered to express a single affinity-tagged HLA-II allele. Over 60 HLA-II alleles have been characterized to date. We demonstrate that neonmhc2 outperforms NetMHCIIpan, the current benchmark for class II prediction, in distinguishing 1) HLA-II ligands presented in cell lines and tissues, and critically, 2) immunogenic CD4+ epitopes identified with tetramer-guided epitope mapping. Furthermore, we show that numerous neoantigen peptides that were ranked highly by neonmhc2 but poorly by NetMHCIIpan were immunogenic in an ex vivo induction. We next studied HLA-II antigen processing in order to further boost our ability to predict CD4+ epitopes. We determined a gene-level bias by comparing transcript expression and gene length to the frequency of observations in MS, finding that secreted genes are over-represented in HLA-II ligandomes from tissues. We built numerous primary sequence-based processability predictors trained on MS data, but only achieved significant prediction improvement when using the simple feature of determining if a candidate peptide contained sequence that overlapped a previously observed HLA-II ligand. By combining neonmhc2 binding prediction, transcript expression, gene bias, and the overlap feature in an integrated presentation predictor, we were able to achieve up to a 61-fold increase in ability to predict HLA-II peptides presented from tissue MS data over NetMHCIIpan alone. We also sought to understand which cells are presenting HLA-II ligands in the tumor microenvironment to elucidate the presentation pathway most relevant to immunotherapy. By leveraging publicly available RNA-seq (bulk and single cell), we found that professional antigen-presenting cells rather than tumor cells are primarily responsible for HLA-II presentation. We developed a novel SILAC-based MS workflow to directly interrogate peptides derived from phagocytosed tumor cells that are presented by dendritic cells. The experiment revealed that mitochondrial genes are preferentially presented from phagocytosed cells. In conclusion, by integrating proteomics and genomics data at large scale, we have defined new rules for understanding HLA-II processing and presentation, particularly in the context of the tumor microenvironment. This work should enhance our ability to predict CD4+ epitopes for immunotherapy. Citation Format: Dewi Harjanto, Jennifer G. Abelin, Matthew Malloy, Prerna Suri, Tyler Colson, Scott P. Goulding, Amanda L. Creech, Lia R. Serrano, Gibbs Nasir, Yusuf Nasrullah, Christopher D. McGann, Diana Velez, Ying S. Ting, Asaf Poran, Daniel A. Rothenberg, Sagar Chhangawala, Alex Rubinsteyn, Jeff Hammerbacher, Richard B. Gaynor, Edward F. Fritsch, Rob C. Oslund, Dominik Barthelme, Terri A. Addona, Christina M. Arieta, Michael S. Rooney. Enhanced HLA-II epitope prediction for immunotherapy with novel proteomics and genomics approaches [abstract]. In: Proceedings of the AACR Special Conference on Tumor Immunology and Immunotherapy; 2019 Nov 17-20; Boston, MA. Philadelphia (PA): AACR; Cancer Immunol Res 2020;8(3 Suppl):Abstract nr B23.

Background: Neoantigens arise from DNA mutations in cancer cells and are important targets for T cell mediated anti-tumor immunity. NEO-PV-01 is a personal neoantigen vaccine of up to 20 peptides designed based on a patient’s neoantigen and HLA profile that is directed at inducing tumor-specific T cell responses to neoantigens. Here, we report relationships between baseline tumor characteristics, immune response and clinical outcome for NT-001, a Phase 1b study of NEO-PV-01 + adjuvant in combination with nivolumab in either first or later line therapy for patients with metastatic melanoma, bladder and non-small cell lung cancer (NCT02897765). This analysis is focused on the melanoma cohort. Methods: Serial blood and tumor biopsies were scheduled for collection: i) prior to treatment, ii) after 12 weeks of nivolumab monotherapy and iii) after completion of NEO-PV-01 vaccination. Baseline features and immune responses in tumor cells were characterized by immunohistochemistry for multiple immune and tumor markers, gene expression, whole exome and TCR sequencing. Antigen-specific responses by IFNγ ELISpot, intracellular cytokine staining, multi-parameter surface and functional phenotyping by FACS and the presence of cytolytic properties were monitored in serial peripheral blood samples. Durability of immune responses up to 104 weeks post start of treatment will be measured. Results: A cohort of 10 melanoma patients demonstrated CD4 and CD8 T cell responses against 56% of vaccine peptides that were detected primarily in the post-vaccination samples as measured by IFNγ ELISpot. Analysis of additional patients is ongoing. T cell responses were neoantigen-specific for most peptides tested (86%; 12/14). Vaccine-induced immune responses were durable in patients who reached the week 52 treatment timepoint. Most T cell responses were polyfunctional, as evident by secretion of multiple cytokines, exhibited a memory and effector memory phenotype and were cytolytic. Epitope spreading, defined as post-vaccination T cell responses to neoantigens not included in the vaccine, was observed in multiple melanoma patients analyzed and evaluated for association with post vaccine clinical responses. Further, multi-platform assessments of immune and molecular responses including gene expression and TCR repertoire analysis demonstrate extensive responses in patients continuing study past 52 weeks. Additional correlates of clinical outcomes with molecular and immunologic responses will be presented. Conclusions: Treatment with NEO-PV-01 + adjuvant in combination with nivolumab induced broad de novo neoantigen-specific immune responses in metastatic melanoma. Immune responses were specific and correlations with clinical outcomes will be discussed. Citation Format: Siwen Hu-Lieskovan, Patrick A. Ott, Aung Naing, Rana H. Besada, Samantha J. Gates, Victoria R. Kohler, Riley R. Curran, Meghan E. Bushway, Julian Scherer, Kristen N. Balogh, Tracey E. Sciuto, Ying S. Ting, Michael S. Rooney, Dewi Harjanto, Zhengping Huang, Yuting Huang, Yvonne Ware, April Lamb, Lisa D. Cleary, Melissa A. Moles, Richard B. Gaynor, Matthew J. Goldstein, Les H. Brail, Joel Greshock, Lakshmi Srinivasan. The personalized vaccine, NEO-PV-01 with anti-PD1, induces neoantigen-specific de novo immune responses in patients with advanced metastatic melanoma: Association with clinical outcomes [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 942.

RNA editing is a mutational mechanism that specifically alters the nucleotide content in transcribed RNA. However, editing rates vary widely, and could result from equivalent editing amongst individual cells, or represent an average of variable editing within a population. Here we present a hierarchical Bayesian model that quantifies the variance of editing rates at specific sites using RNA-seq data from both single cells, and a cognate bulk sample to distinguish between these two possibilities. The model predicts high variance for specific edited sites in murine macrophages and dendritic cells, findings that we validated experimentally by using targeted amplification of specific editable transcripts from single cells. The model also predicts changes in variance in editing rates for specific sites in dendritic cells during the course of LPS stimulation. Our data demonstrate substantial variance in editing signatures amongst single cells, supporting the notion that RNA editing generates diversity within cellular populations., RNA editing rate detected from bulk RNA-seq data can vary widely. Here, by constructing a hierarchical Bayesian model, the authors report substantial variance in editing signatures detected by RNA-seq data from both single cells and a cognate bulk sample.

Adenosine-to-inosine (A-to-I) RNA editing is a widespread post-transcriptional mechanism, but its genomic landscape and clinical relevance in cancer have not been investigated systematically. We characterized the global A-to-I RNA editing profiles of 6,236 patient samples of 17 cancer types from The Cancer Genome Atlas and revealed a striking diversity of altered RNA-editing patterns in tumors relative to normal tissues. We identified an appreciable number of clinically relevant editing events, many of which are in noncoding regions. We experimentally demonstrated the effects of several cross-tumor nonsynonymous RNA editing events on cell viability and provide the evidence that RNA editing could selectively affect drug sensitivity. These results highlight RNA editing as an exciting theme for investigating cancer mechanisms, biomarkers, and treatments.

Biological systems provide us with a diverse source of peptide-based ligands for cellular adhesion. Controlling and assessing the ligand surface density as well as tailoring the surface chemistry to have specific cellular adhesion properties are important in biomaterials design. In the following work, we provide a means for displaying peptide-based ligands on magnetic liposomes in which the surface density and chemistry may be controlled. Simultaneously, the conjugated vesicles provide a fluorescent signal for examining steric hindrance among surface ligands. In addition, the inherent magnetic and fluorescence features of this system revealed potential for magnet-based cell isolation and fluorescent labeling of adhered cells, respectively. Adhered cells were found to remain viable and proliferative, thereby allowing them to be used for subsequent evaluation. In a specific demonstration, we control the density of fibronectin-mimetic ligands on the polydiacetylene liposome surfaces. We find that steric limitation occurring at over 20% surface density result in decreased cell adhesion, in accord with related techniques. The magnetic-liposome system offers the means for not only separating cells adhered to the biomaterial, but also providing the ability to control and assess the biomaterial surface. This may prove particularly useful for examining combinations of peptide-based ligands or for evaluating the molecular-level ligand accessibility and its effect on cell attachment to a biomaterial surface.

Extracellular matrix (ECM) remodeling is a key physiological process that occurs in a number of contexts, including cell migration, and is especially important for cellular form and function in three-dimensional (3D) matrices. However, there have been few attempts to computationally model how cells modify their environment in a manner that accounts for both cellular properties and the architecture of the surrounding ECM. To this end, we have developed and validated a novel model to simulate matrix remodeling that explicitly defines cells in a 3D collagenous matrix. In our simulation, cells can degrade, deposit, or pull on local fibers, depending on the fiber density around each cell. The cells can also move within the 3D matrix. Different cell phenotypes can be modeled by varying key cellular parameters. Using the model we have studied how two model cancer cell lines, of differing invasiveness, modify matrices with varying fiber density in their vicinity by tracking the metric of fraction of matrix occupied by fibers. Our results quantitatively demonstrate that in low density environments, cells deposit more collagen to uniformly increase fibril fraction. On the other hand, in higher density environments, the less invasive model cell line reduced the fibril fraction as compared to the highly invasive phenotype. These results show good qualitative and quantitative agreement with existing experimental literature. Our simulation is therefore able to function as a novel platform to provide new insights into the clinically relevant and physiologically critical process of matrix remodeling by helping identify critical parameters that dictate cellular behavior in complex native-like environments.

Cell adhesions to both soluble and insoluble extracellular matrix ligands are critical in inter and intra-cellular signaling that mediates numerous physiological processes. These adhesions are complex structures composed of many scaffolding and signaling proteins. There are four distinct types of cell–matrix adhesions: focal complexes, focal adhesions, fibrillar adhesions, and 3D cell–matrix adhesions, which vary in composition, organization and function. The primary mediators of cell–matrix adhesions are integrins, which are mechanosensitive transmembrane receptor proteins that directly bind to matrix ligands to initiate adhesion formation. The development of cell–matrix adhesions is affected by a number of factors including matrix properties such as dimensionality and rigidity, and forces, both internally and externally generated, exerted on the adhesion sites. In this article, we discuss how matrix mechanics and forces affect the assembly and maturation of cell–matrix adhesions.

Cell adhesions to both soluble and insoluble extracellular matrix ligands are critical in inter and intra-cellular signaling that mediates numerous physiological processes. These adhesions are complex structures composed of many scaffolding and signaling proteins. There are four distinct types of cell–matrix adhesions: focal complexes, focal adhesions, fibrillar adhesions, and 3D cell–matrix adhesions, which vary in composition, organization and function. The primary mediators of cell–matrix adhesions are integrins, which are mechanosensitive transmembrane receptor proteins that directly bind to matrix ligands to initiate adhesion formation. The development of cell–matrix adhesions is affected by a number of factors including matrix properties such as dimensionality and rigidity, and forces, both internally and externally generated, exerted on the adhesion sites. In this article, we discuss how matrix mechanics and forces affect the assembly and maturation of cell–matrix adhesions.

Cell migration is a fundamental process that is crucial to a variety of physiological events. While traditional approaches have focused on two-dimensional (2D) systems, recent efforts have shifted to studying migration in three-dimensional (3D) matrices. A major distinction that has emerged is the increased importance of cell-matrix interactions in 3D environments. In particular, cell motility in 3D matrices is more dependent on matrix metalloproteinases (MMPs) to degrade steric obstacles than in 2D systems. In this study, we implement the effects of MMP-mediated proteolysis in a force-based computational model of 3D migration, testing two matrix ligand-MMP relationships that have been observed experimentally: linear and log-linear. The model for both scenarios predicts maximal motility at intermediate matrix ligand and MMP levels, with the linear case providing more physiologically compelling results. Recent experimental results suggesting MMP influence on integrin expression are also integrated into the model. While the biphasic behavior is retained, with MMP-integrin feedback peak cell speed is observed in a low ligand, high MMP regime instead of at intermediate ligand and MMP levels for both ligand-MMP relationships. The simulation provides insight into the expanding role of cell-matrix interactions in cell migration in 3D environments and has implications for cancer research.