Programable Albumin-Hitchhiking Nanobodies Enhance the Delivery of STING Agonists to Potentiate Cancer Immunotherapy.

Stimulator of interferon genes (STING) is a promising target for potentiating antitumor immunity, but multiple pharmacological barriers limit the clinical utility, efficacy, and/or safety of STING agonists. Here we describe a modular platform for systemic administration of STING agonists based on nanobodies engineered for in situ hitchhiking of agonist cargo on serum albumin. Using site-selective bioconjugation chemistries to produce molecularly defined products, we found that covalent conjugation of a STING agonist to anti-albumin nanobodies improved pharmacokinetics and increased cargo accumulation in tumor tissue, stimulating innate immune programs that increased the infiltration of activated natural killer cells and T cells, which potently inhibited tumor growth in multiple mouse tumor models. We also demonstrated the programmability of the platform through the recombinant integration of a second nanobody domain that targeted programmed cell death ligand-1 (PD-L1), which further increased cargo delivery to tumor sites while also blocking immunosuppressive PD-1/PD-L1 interactions. This bivalent nanobody carrier for covalently conjugated STING agonists stimulated robust antigen-specific T cell responses and long-lasting immunological memory, conferred enhanced therapeutic efficacy, and was effective as a neoadjuvant treatment for improving responses to adoptive T cell transfer therapy. Albumin-hitchhiking nanobodies thus offer an enabling, multimodal, and programmable platform for systemic delivery of STING agonists with potential to augment responses to multiple immunotherapeutic modalities.

Curating retrospective multimodal and longitudinal data for community cohorts at risk for lung cancer.

BACKGROUND: Large community cohorts are useful for lung cancer research, allowing for the analysis of risk factors and development of predictive models. OBJECTIVE: A robust methodology for (1) identifying lung cancer and pulmonary nodules diagnoses as well as (2) associating multimodal longitudinal data with these events from electronic health record (EHRs) is needed to optimally curate cohorts at scale. METHODS: In this study, we leveraged (1) SNOMED concepts to develop ICD-based decision rules for building a cohort that captured lung cancer and pulmonary nodules and (2) clinical knowledge to define time windows for collecting longitudinal imaging and clinical concepts. We curated three cohorts with clinical data and repeated imaging for subjects with pulmonary nodules from our Vanderbilt University Medical Center. RESULTS: Our approach achieved an estimated sensitivity 0.930 (95% CI: [0.879, 0.969]), specificity of 0.996 (95% CI: [0.989, 1.00]), positive predictive value of 0.979 (95% CI: [0.959, 1.000]), and negative predictive value of 0.987 (95% CI: [0.976, 0.994]) for distinguishing lung cancer from subjects with SPNs. CONCLUSION: This work represents a general strategy for high-throughput curation of multi-modal longitudinal cohorts at risk for lung cancer from routinely collected EHRs.

Unbiased estimation of the Hessian for partially observed diffusions.

In this article, we consider the development of unbiased estimators of the Hessian, of the log-likelihood function with respect to parameters, for partially observed diffusion processes. These processes arise in numerous applications, where such diffusions require derivative information, either through the Jacobian or Hessian matrix. As time-discretizations of diffusions induce a bias, we provide an unbiased estimator of the Hessian. This is based on using Girsanov's Theorem and randomization schemes developed through Mcleish (2011 Monte Carlo Methods Appl. 17, 301-315 (doi:10.1515/mcma.2011.013)) and Rhee & Glynn (2016 Op. Res. 63, 1026-1043). We demonstrate our developed estimator of the Hessian is unbiased, and one of finite variance. We numerically test and verify this by comparing the methodology here to that of a newly proposed particle filtering methodology. We test this on a range of diffusion models, which include different Ornstein-Uhlenbeck processes and the Fitzhugh-Nagumo model, arising in neuroscience.

Highly motile cells are metabolically responsive to collagen density.

Altered tissue mechanics and metabolism have gained significant attention as drivers of tumorigenesis, and mechanoresponsive metabolism has been implicated in migration and metastasis. However, heterogeneity in cell populations makes it difficult to link changes in behavior with metabolism, as individual cell behaviors are not necessarily reflected in population-based measurements. As such, the impact of increased collagen deposition, a tumor-associated collagen signature, on metabolism remains ambiguous. Here, we utilize a wide range of collagen densities to alter migration ability and study the bioenergetics of individual cells over time. Sorting cells based on their level of motility revealed energetics are a function of collagen density only for highly motile cells, not the entire population or cells with low motility. Changes in migration with increasing collagen density were correlated with cellular energetics, where matrix conditions most permissive to migration required less energy usage during movement and migrated more efficiently. These findings reveal a link between matrix mechanics, migratory phenotype, and bioenergetics and suggest that energetic costs are determined by the extracellular matrix and influence cell motility.

Microscale Interrogation of 3D Tissue Mechanics.

Cells in vivo live in a complex microenvironment composed of the extracellular matrix (ECM) and other cells. Growing evidence suggests that the mechanical interaction between the cells and their microenvironment is of critical importance to their behaviors under both normal and diseased conditions, such as migration, differentiation, and proliferation. The study of tissue mechanics in the past two decades, including the assessment of both mechanical properties and mechanical stresses of the extracellular microenvironment, has greatly enriched our knowledge about how cells interact with their mechanical environment. Tissue mechanical properties are often heterogeneous and sometimes anisotropic, which makes them difficult to obtain from macroscale bulk measurements. Mechanical stresses were first measured for cells cultured on two-dimensional (2D) surfaces with well-defined mechanical properties. While 2D measurements are relatively straightforward and efficient, and they have provided us with valuable knowledge on cell-ECM interactions, that knowledge may not be directly applicable to in vivo systems. Hence, the measurement of tissue stresses in a more physiologically relevant three-dimensional (3D) environment is required. In this mini review, we will summarize and discuss recent developments in using optical, magnetic, genetic, and mechanical approaches to interrogate 3D tissue stresses and mechanical properties at the microscale.

A Novel Cell Penetrating Peptide for the Differentiation of Human Neural Stem Cells.

Retinoic acid (RA) is a bioactive lipid that has been shown to promote neural stem cell differentiation. However, the highly hydrophobic molecule needs to first solubilize and translocate across the cell membrane in order to exert a biological response. The cell entry of RA can be aided by cell penetrating peptides (CPPs), which are short amino acid sequences that are able to carry bioactive cargo past the cell membrane. In this work, a novel cell penetrating peptide was developed to deliver RA to human neural stem cells and, subsequently, promote neuronal differentiation. The novel CPP consists of a repeating sequence, whose number of repeats is proportional to the efficiency of cell penetration. Using fluorescence microscopy, the mode of translocation was determined to be related to an endocytic pathway. The levels of β-III tubulin (Tubb3) and microtubule associated protein 2 (MAP2) expression in neural stem cells treated with RA conjugated to the CPP were assessed by quantitative immunocytochemistry.