Designing meaningful continuous representations of T cell receptor sequences with deep generative models.

T Cell Receptor (TCR) antigen binding underlies a key mechanism of the adaptive immune response yet the vast diversity of TCRs and the complexity of protein interactions limits our ability to build useful low dimensional representations of TCRs. To address the current limitations in TCR analysis we develop a capacity-controlled disentangling variational autoencoder trained using a dataset of approximately 100 million TCR sequences, that we name TCR-VALID. We design TCR-VALID such that the model representations are low-dimensional, continuous, disentangled, and sufficiently informative to provide high-quality TCR sequence de novo generation. We thoroughly quantify these properties of the representations, providing a framework for future protein representation learning in low dimensions. The continuity of TCR-VALID representations allows fast and accurate TCR clustering and is benchmarked against other state-of-the-art TCR clustering tools and pre-trained language models.

LTßR Agonism Promotes Anti-Tumor Immune Responses via Modulation of the Tumor Microenvironment.

The presence of high endothelial venules (HEV) and tertiary lymphoid structures (TLS) in solid tumors is correlated with favorable prognosis and better responses to immune-checkpoint blockade (ICB) in many cancer types. Elucidation of the molecular mechanisms underlying intratumoral HEV and TLS formation and their contribution to anti-tumor responses may facilitate development of improved treatment strategies. Lymphotoxin beta receptor (LTßR) signaling is a critical regulator of lymph node organogenesis and can cooperate with antiangiogenic and ICB treatment to augment tumor-associated HEV formation. Here, we demonstrated that LTßR signaling modulates the tumor microenvironment via multiple mechanisms to promote anti-tumor T cell responses. Systemic activation of the LTßR pathway via agonistic antibody treatment induced tumor-specific HEV formation, upregulated the expression of TLS-related chemokines, and enhanced dendritic cell (DC) and T cell infiltration and activation in syngeneic tumor models. In vitro studies confirmed direct effects of LTßR agonism on DC activation and maturation and associated DC-mediated T cell activation. Single agent LTßR agonist treatment inhibited syngeneic tumor growth in a CD8+ T cell- and HEV-dependent manner, and the LTßR agonist enhanced anti-tumor effects of anti-PD-1 and CAR T cell therapies. An in vivo tumor screen for TLS-inducing cytokines revealed that the combination of LTßR agonism and lymphotoxin alpha (LT⍺) expression promoted robust intratumoral TLS induction and enhanced tumor responses to anti-CTLA-4 treatment. Collectively, this study highlights crucial functions of LTßR signaling in modulating the tumor microenvironment and could inform future HEV/TLS-based strategies for cancer treatments.

Digital wearable insole-based identification of knee arthropathies and gait signatures using machine learning.

Gait is impaired in musculoskeletal conditions, such as knee arthropathy. Gait analysis is used in clinical practice to inform diagnosis and monitor disease progression or intervention response. However, clinical gait analysis relies on subjective visual observation of walking as objective gait analysis has not been possible within clinical settings due to the expensive equipment, large-scale facilities, and highly trained staff required. Relatively low-cost wearable digital insoles may offer a solution to these challenges. In this work, we demonstrate how a digital insole measuring osteoarthritis-specific gait signatures yields similar results to the clinical gait-lab standard. To achieve this, we constructed a machine learning model, trained on force plate data collected in participants with knee arthropathy and controls. This model was highly predictive of force plate data from a validation set (area under the receiver operating characteristics curve [auROC] = 0.86; area under the precision-recall curve [auPR] = 0.90) and of a separate, independent digital insole dataset containing control and knee osteoarthritis subjects (auROC = 0.83; auPR = 0.86). After showing that digital insole-derived gait characteristics are comparable to traditional gait measurements, we next showed that a single stride of raw sensor time-series data could be accurately assigned to each subject, highlighting that individuals using digital insoles can be identified by their gait characteristics. This work provides a framework for a promising alternative to traditional clinical gait analysis methods, adds to the growing body of knowledge regarding wearable technology analytical pipelines, and supports clinical development of at-home gait assessments, with the potential to improve the ease, frequency, and depth of patient monitoring.

The way we walk ­ our 'gait' ­ is a key indicator of health. Gait irregularities like limping, shuffling or a slow pace can be signs of muscle or joint problems. Assessing a patient's gait is therefore an important element in diagnosing these conditions, and in evaluating whether treatments are working. Gait is often assessed via a simple visual inspection, with patients being asked to walk back and forth in a doctor's office. While quick and easy, this approach is highly subjective and therefore imprecise. 'Objective gait analysis' is a more accurate alternative, but it relies on tests being conducted in specialised laboratories with large-scale, expensive equipment operated by highly trained staff. Unfortunately, this means that gait laboratories are not accessible for everyday clinical use. In response, Wipperman et al. aimed to develop a low-cost alternative to the complex equipment used in gait laboratories. To do this, they harnessed wearable sensor technologies ­ devices that can directly measure physiological data while embedded in clothing or attached to the user. Wearable sensors have the advantage of being cheap, easy to use, and able to provide clinically useful information without specially trained staff. Wipperman et al. analysed data from classic gait laboratory devices, as well as 'digital insoles' equipped with sensors that captured foot movements and pressure as participants walked. The analysis first 'trained' on data from gait laboratories (called force plates) and then applied the method to gait measurements obtained from digital insoles worn by either healthy participants or patients with knee problems. Analysis of the pressure data from the insoles confirmed that they could accurately predict which measurements were from healthy individuals, and which were from patients. The gait characteristics detected by the insoles were also comparable to lab-based measurements ­ in other words, the insoles provided similar type and quality of data as a gait laboratory. Further analysis revealed that information from just a single step could reveal additional information about the subject's walking. These results support the use of wearable devices as a simple and relatively inexpensive way to measure gait in everyday clinical practice, without the need for specialised laboratories and visits to the doctor's office. Although the digital insoles will require further analytical and clinical study before they can be widely used, Wipperman et al. hope they will eventually make monitoring muscle and joint conditions easier and more affordable.

Identification of pan-cancer/testis genes and validation of therapeutic targeting in triple-negative breast cancer: Lin28a-based and Siglece-based vaccination induces antitumor immunity and inhibits metastasis.

BACKGROUND: Cancer-testis (CT) genes are targets for tumor antigen-specific immunotherapy given that their expression is normally restricted to the immune-privileged testis in healthy individuals with aberrant expression in tumor tissues. While they represent targetable germ tissue antigens and play important functional roles in tumorigenesis, there is currently no standardized approach for identifying clinically relevant CT genes. Optimized algorithms and validated methods for accurate prediction of reliable CT antigens (CTAs) with high immunogenicity are also lacking. METHODS: Sequencing data from the Genotype-Tissue Expression (GTEx) and The Genomic Data Commons (GDC) databases was used for the development of a bioinformatic pipeline to identify CT exclusive genes. A CT germness score was calculated based on the number of CT genes expressed within a tumor type and their degree of expression. The impact of tumor germness on clinical outcome was evaluated using healthy GTEx and GDC tumor samples. We then used a triple-negative breast cancer mouse model to develop and test an algorithm that predicts epitope immunogenicity based on the identification of germline sequences with strong major histocompatibility complex class I (MHCI) and MHCII binding affinities. Germline sequences for CT genes were synthesized as long synthetic peptide vaccines and tested in the 4T1 triple-negative model of invasive breast cancer with Poly(I:C) adjuvant. Vaccine immunogenicity was determined by flow cytometric analysis of in vitro and in vivo T-cell responses. Primary tumor growth and lung metastasis was evaluated by histopathology, flow cytometry and colony formation assay. RESULTS: We developed a new bioinformatic pipeline to reliably identify CT exclusive genes as immunogenic targets for immunotherapy. We identified CT genes that are exclusively expressed within the testis, lack detectable thymic expression, and are significantly expressed in multiple tumor types. High tumor germness correlated with tumor progression but not with tumor mutation burden, supporting CTAs as appealing targets in low mutation burden tumors. Importantly, tumor germness also correlated with markers of antitumor immunity. Vaccination of 4T1 tumor-bearing mice with Siglece and Lin28a antigens resulted in increased T-cell antitumor immunity and reduced primary tumor growth and lung metastases. CONCLUSION: Our results present a novel strategy for the identification of highly immunogenic CTAs for the development of targeted vaccines that induce antitumor immunity and inhibit metastasis.