Challenges and Progress in Designing Broad-Spectrum Vaccines Against Rapidly Mutating Viruses.

Viruses evolve to evade prior immunity, causing significant disease burden. Vaccine effectiveness deteriorates as pathogens mutate, requiring redesign. This is a problem that has grown worse due to population increase, global travel, and farming practices. Thus, there is significant interest in developing broad-spectrum vaccines that mitigate disease severity and ideally inhibit disease transmission without requiring frequent updates. Even in cases where vaccines against rapidly mutating pathogens have been somewhat effective, such as seasonal influenza and SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2), designing vaccines that provide broad-spectrum immunity against routinely observed viral variation remains a desirable but not yet achieved goal. This review highlights the key theoretical advances in understanding the interplay between polymorphism and vaccine efficacy, challenges in designing broad-spectrum vaccines, and technology advances and possible avenues forward. We also discuss data-driven approaches for monitoring vaccine efficacy and predicting viral escape from vaccine-induced protection. In each case, we consider illustrative examples in vaccine development from influenza, SARS-CoV-2, and HIV (human immunodeficiency virus)-three examples of highly prevalent rapidly mutating viruses with distinct phylogenetics and unique histories of vaccine technology development.

Hierarchical confounder discovery in the experiment-machine learning cycle.

The promise of machine learning (ML) to extract insights from high-dimensional datasets is tempered by confounding variables. It behooves scientists to determine if a model has extracted the desired information or instead fallen prey to bias. Due to features of natural phenomena and experimental design constraints, bioscience datasets are often organized in nested hierarchies that obfuscate the origins of confounding effects and render confounder amelioration methods ineffective. We propose a non-parametric statistical method called the rank-to-group (RTG) score that identifies hierarchical confounder effects in raw data and ML-derived embeddings. We show that RTG scores correctly assign the effects of hierarchical confounders when linear methods fail. In a public biomedical image dataset, we discover unreported effects of experimental design. We then use RTG scores to discover crossmodal correlated variability in a multi-phenotypic biological dataset. This approach should be generally useful in experiment-analysis cycles and to ensure confounder robustness in ML models.

Setwise comparison: efficient fine-grained rating of movement videos using algorithmic support - a proof of concept study.

Purpose: Clinical ordinal rating scales of movements, e.g., the Expanded Disability Status Scale, have poor intra- and interrater reliability, are insensitive to subtle differences and result in coarse-grained ratings compared to relative comparative rating methods. We therefore established video-based setwise comparison as a fine-grained, reliable and efficient rating method of motor dysfunction using algorithmic support.Materials and methods: Eight neurologists rated a set of 40 multiple sclerosis patient videos of the Finger-to-Nose-Test using both the newly developed setwise comparison and the established pairwise comparison techniques, which result in a continuous rating scale. Reliability was assessed by the intra-class correlation coefficient. Construct validity was estimated as Pearson's correlation between the continuous scale and severity ratings according to the Neurostatus scale for upper-extremity tremor/dysmetria and the Nine-hole-peg-test. Comparing the time needed for ratings assessed efficiency.Results: Intra-class correlation coefficient was 0.83 for setwise and 0.7 for pairwise comparison. Correlation to the tremor/dysmetria score of the Neurostatus was 0.86 for both rating procedures and correlation to the Nine-hole-peg-test was 0.64 (setwise) and 0.66 (pairwise). The time needed to rate 40 videos was 22.9 ± 6.9 minutes (setwise) and 77.8 ± 14.5 minutes (pairwise).Conclusions: Setwise comparison is an efficient, valid and reliable method for fine-grained rating of motor dysfunction that can be applied to larger datasets. It is substantially more efficient than pairwise comparison.Implications for rehabilitationDisability rating is crucial in clinical neurorehabilitation and in clinical trials.Humans are naturally inconsistent in rating items on ordinal scales leading to poor intra- and interrater reliability, insensitivity to subtle differences and coarse-grained ratings.Video-based setwise comparison is a new rating method enabling fine-grained, reliable and efficient ratings of motor dysfunction using algorithmic support.

Bifunctional immune checkpoint-targeted antibody-ligand traps that simultaneously disable TGFß enhance the efficacy of cancer immunotherapy.

A majority of cancers fail to respond to immunotherapy with antibodies targeting immune checkpoints, such as cytotoxic T-lymphocyte antigen-4 (CTLA-4) or programmed death-1 (PD-1)/PD-1 ligand (PD-L1). Cancers frequently express transforming growth factor-ß (TGFß), which drives immune dysfunction in the tumor microenvironment by inducing regulatory T cells (Tregs) and inhibiting CD8+ and TH1 cells. To address this therapeutic challenge, we invent bifunctional antibody-ligand traps (Y-traps) comprising an antibody targeting CTLA-4 or PD-L1 fused to a TGFß receptor II ectodomain sequence that simultaneously disables autocrine/paracrine TGFß in the target cell microenvironment (a-CTLA4-TGFßRIIecd and a-PDL1-TGFßRIIecd). a-CTLA4-TGFßRIIecd is more effective in reducing tumor-infiltrating Tregs and inhibiting tumor progression compared with CTLA-4 antibody (Ipilimumab). Likewise, a-PDL1-TGFßRIIecd exhibits superior antitumor efficacy compared with PD-L1 antibodies (Atezolizumab or Avelumab). Our data demonstrate that Y-traps counteract TGFß-mediated differentiation of Tregs and immune tolerance, thereby providing a potentially more effective immunotherapeutic strategy against cancers that are resistant to current immune checkpoint inhibitors.

Inhibition of TGF-ß enhances the in vivo antitumor efficacy of EGF receptor-targeted therapy.

EGF receptor (EGFR)-targeted monoclonal antibodies (mAb), such as cetuximab, execute their antitumor effect in vivo via blockade of receptor-ligand interactions and engagement of Fcγ receptors on immune effector cells that trigger antibody-dependent cell-mediated cytotoxicity (ADCC). We show that tumors counteract the in vivo antitumor activity of anti-EGFR mAbs by increasing tumor cell-autonomous expression of TGF-ß. We show that TGF-ß suppresses the expression of key molecular effectors of immune cell-mediated cytotoxicity, including Apo2L/TRAIL, CD95L/FasL, granzyme B, and IFN-γ. In addition to exerting an extrinsic inhibition of the cytotoxic function of immune effectors, TGF-ß-mediated activation of AKT provides an intrinsic EGFR-independent survival signal that protects tumor cells from immune cell-mediated apoptosis. Treatment of mice-bearing xenografts of human head and neck squamous cell carcinoma with cetuximab resulted in emergence of resistant tumor cells that expressed relatively higher levels of TGF-ß compared with untreated tumor-bearing mice. Although treatment with cetuximab alone forced the natural selection of TGF-ß-overexpressing tumor cells in nonregressing tumors, combinatorial treatment with cetuximab and a TGF-ß-blocking antibody prevented the emergence of such resistant tumor cells and induced complete tumor regression. Therefore, elevated levels of TGF-ß in the tumor microenvironment enable tumor cells to evade ADCC and resist the antitumor activity of cetuximab in vivo. Our results show that TGF-ß is a key molecular determinant of the de novo and acquired resistance of cancers to EGFR-targeted mAbs, and provide a rationale for combinatorial targeting of TGF-ß to improve anti-EGFR-specific antibody therapy of EGFR-expressing cancers.