Immune heterogeneity within the tumor microenvironment undoubtedly adds several layers of complexity to our understanding of drug sensitivity and patient prognosis across various cancer types. Within the tumor microenvironment, immunogenicity is a favorable clinical feature in part driven by the antitumor activity of CD8+ T cells. However, tumors often inhibit this antitumor activity by exploiting the suppressive function of regulatory T cells (Tregs), thus suppressing the adaptive immune response. Despite the seemingly intuitive immunosuppressive biology of Tregs, prognostic studies have produced contradictory results regarding the relationship between Treg enrichment and survival. We therefore analyzed RNA-seq data of Treg-enriched tumor samples to derive a pan-cancer gene signature able to help reconcile the inconsistent results of Treg studies, by better understanding the variable clinical association of Tregs across alternative tumor contexts. We show that increased expression of a 32-gene signature in Treg-enriched tumor samples (n = 135) is able to distinguish a cohort of patients associated with chemosensitivity and overall survival. This cohort is also enriched for CD8+ T cell abundance, as well as the antitumor M1 macrophage subtype. With a subsequent validation in a larger TCGA pool of Treg-enriched patients (n = 626), our results reveal a gene signature able to produce unsupervised clusters of Treg-enriched patients, with one cluster of patients uniquely representative of an immunogenic tumor microenvironment. Ultimately, these results support the proposed gene signature as a putative biomarker to identify certain Treg-enriched patients with immunogenic tumors that are more likely to be associated with features of favorable clinical outcome.

In this work we aim to capitalize on the availability of Internet image search engines to automatically create image training sets from user provided queries. This problem is particularly difficult due to the low precision of image search results. Unlike many existing dataset gathering approaches, we do not assume a category model based on a small subset of the noisy data or an ad-hoc validation set. Instead we use a nonparametric measure of strangeness [8] in the space of holistic image representations, and perform an iterative feature elimination algorithm to remove the most strange examples from the category. This is the equivalent of keeping only features that are found to be consistent with others in the class. We show that applying our method to image search data before training improves average recognition performance, and demonstrate that we obtain comparative precision and recall results to the current state of the art, all the while maintaining a significantly simpler approach. In the process we also extend the strangeness-based feature elimination algorithm to automatically select good threshold values and perform filtering of a single class when the background is given.

We investigate dynamical models of human motion that can support both synthesis and analysis tasks. Unlike coarser discriminative models that work well when action classes are nicely separated, we seek models that have finescale representational power and can therefore model subtle differences in the way an action is performed. To this end, we model an observed action as an (unknown) linear time-invariant dynamical model of relatively small order, driven by a sparse bounded input signal.Our motivating intuition is that the time-invariant dynamics will capture the unchanging physical characteristics of an actor, while the inputs used to excite the system will correspond to a causal signature of the action being performed. We show that our model has sufficient representational power to closely approximate large classes of non-stationary actions with significantly reduced complexity. We also show that temporal statistics of the inferred input sequences can be compared in order to recognize actions and detect transitions between them.

In response to the need for a safe, efficacious vaccine that provides broad immune protection against SARS-CoV-2 infection, we have developed a dual-antigen COVID-19 vaccine. The vaccine delivers both the viral spike (S) protein modified to increase cell-surface expression (S-Fusion) and the viral nucleocapsid (N) protein with an Enhanced T-cell Stimulation Domain (N-ETSD) to enhance MHC class I and II presentation and T-cell responses. The vaccine antigens are delivered using a human adenovirus serotype 5 (hAd5) platform with E1, E2b, and E3 regions deleted that has been shown in previous cancer vaccine studies to be effective in the presence of pre-existing hAd5 immunity. Here, we demonstrate the hAd5 S-Fusion + N-ETSD (hAd5 S + N) vaccine antigens when expressed by dendritic cells (DCs) of previously SARS-CoV-2-infected patients elicit Th1 dominant activation of autologous patient T cells, indicating the vaccine antigens have the potential for generating immune responses in patients previously infected or vaccinated. We further demonstrate that participants in our open-label Phase 1b study of the dual-antigen hAd5 S + N vaccine generate Th1 dominant S- and N-specific T cells after a single prime subcutaneous injection and that the magnitude of these responses were comparable to those seen for T cells from previously infected patients. We further present our in silico prediction of T-cell epitope HLA binding for both the first-wave SARS-CoV-2 ‘A’ strain and the K417N, E484K, and N501Y S as well as the T201I N variants that suggests T-cell responses to the hAd5 S + N vaccine will retain efficacy against these variants. These findings that the dual-antigen hAd5 S + N vaccine elicits SARS-CoV-2-relevant T-cell responses and that such cell-mediated protection is likely to be sustained against emerging variants supports the testing of this vaccine as a universal booster that would enhance and broaden existing immune protection conferred by currently approved S-based vaccines.

DNA accessibility, chromatin regulation, and genome methylation are key drivers of cancer transcription. However, there is much left to be understood about the functional implications of sequence-level data to the regulation of gene expression, especially when it comes to the noncoding genome. Recently [Kelley, D., Snoek, J., and Rinn, J., Genome Res. 2016] trained neural networks to effectively predict DNA accessibility in multiple cell types. These models make it possible to explore the impact of mutations on the predicted accessibility and thus directly link one aspect of the gene regulation puzzle all the way down to the sequence level. We present a model with improved performance on the original dataset of 164 ENCODE and Roadmap Epigenomics Consortium sample types, and then extend the method to provide predictions on any sample with RNA-Seq data without need of DNase-seq for the sample. We first demonstrate that with several model and algorithmic changes we improve performance across 164 cell types from a mean AUC of 0.895 to a mean AUC of 0.910. Unfortunately current accessibility models require DNase-seq for each new cell type. Models for detecting transcription factor binding sites, which rely on ChIP-seq for training data, also share this issue. In order to generalize sequence-based predictive models to apply to unseen cell types without requiring re-training we investigate using RNA-Seq as a proxy signature of cell type. The model aims to capture the interdependence of gene expression levels that characterize a cell with the regulatory logic in which sequence-level signatures are combined to determine accessibility without restriction to cell type. We explore the model’s performance when applied to held-out cell types in the ENCODE and Roadmap Epigenomics Consortium data as well as data from the TCGA Pan-Cancer initiative. We look for the impact of non-coding changes in whole-genome sequencing data in TCGA samples, and report on predicted differences in DNA accessibility across cancer subtypes. Citation Format: Kamil Wnuk, Jeremi Sudol, Shahrooz Rabizadeh, Patrick Soon-Shiong, Christopher Szeto, Charles Vaske. Predicting DNA accessibility in the pan-cancer tumor genome using RNA-Seq, WGS, and deep learning [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 393. doi:10.1158/1538-7445.AM2017-393