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Somatic activity of LINE-1 (L1) mobile elements has been implicated in cancer etiology, which may be related to the loss of p53-mediated regulation as a result ofTP53mutations. Quantifying the mechanisms of L1 regulation in cancer has been challenging. Here, we build a statistical model of L1 regulation by simultaneously quantifying L1 retrotransposition, L1 expression, and the fitness costs of mutatedTP53with precision. We first developed Total ReCall, an algorithm specifically tailored to the mechanisms of L1 reintegration, to detect L1 insertions from short-read whole-genome sequencing. Applying Total ReCall to high-quality data consisting of >750 paired tumor and normal samples from The Cancer Genome Atlas (TCGA) shows high L1 insertion heterogeneity among tumor types, with increased retrotransposition burden in lung squamous cell carcinoma, head and neck, and colon cancers. We next assessed the active RNA expression of intact L1 in >9,000 TCGA tumor samples, establishing, for the first time, a clear correlation between L1 expression and retrotransposition. Finally, we integrated the number of L1 insertions, L1 expression and a mathematical model ofTP53fitness into a multi-modal model of p53- mediated mechanisms of L1 regulation. We show thatTP53mutations enable retrotransposition both by disinhibiting L1 expression and enabling its reintegration and quantify the relative weights of this dual regulatory role. We demonstrate how mechanism-based multi-modal modeling applied at scale can statistically disentangle the complex interplay between canonical driver events in tumor evolution and retrotransposon activity.

Purpose:TNF-related apoptosis inducing ligand (TRAIL) expression by immune cells contributes to antitumor immunity. A naturally occurring splice variant of TRAIL, called TRAILshort, antagonizes TRAIL-dependent cell killing. It is unknown whether tumor cells express TRAILshort and if it impacts antitumor immunity.Experimental Design:We used an unbiased informatics approach to identify TRAILshort expression in primary human cancers, and validated those results with IHC and ISH. TRAILshort-specific mAbs were used to determine the effect of TRAILshort on tumor cell sensitivity to TRAIL, and to immune effector cell dependent killing of autologous primary tumors.Results:As many as 40% of primary human tumors express TRAILshort by both RNA sequencing and IHC analysis. By ISH, TRAILshort expression is present in tumor cells and not bystander cells. TRAILshort inhibition enhances cancer cell lines sensitivity to TRAIL-dependent killing both in vitro and in immunodeficient xenograft mouse models. Immune effector cells isolated from patients with B-cell malignancies killed more autologous tumor cells in the presence compared with the absence of TRAILshort antibody (P < 0.05).Conclusions:These results identify TRAILshort in primary human malignancies, and suggest that TRAILshort blockade can augment the effector function of autologous immune effector cells.See related commentary by de Miguel and Pardo, p. 5546

Supplementary tables and figures

Background Achieving a functional or sterilizing cure for HIV will require identification of therapeutic interventions that reduce HIV reservoir size in infected individuals. Proteasome inhibitors, such as ixazomib, impact multiple aspects of HIV biology including latency, transcription initiation, viral replication, and infected cell killing through the HIV protease – Casp8p41 pathway, resulting in latency reversal and reduced measures of HIV reservoir size ex vivo. Methods We conducted a phase 1b/2a dose escalating, open label trial of weekly oral ixazomib for 24 weeks in antiretroviral (ART)-suppressed, HIV positive adults (NCT02946047). The study was conducted from March 2017 to August 2019 at two tertiary referral centers in the United States. The primary outcomes were safety and tolerability of oral ixazomib. Secondary outcomes included changes in immunologic markers and estimates of HIV reservoir size after ixazomib treatment. Findings Sixteen participants completed the study. Ixazomib up to 4mg weekly was safe and well-tolerated, yielding no treatment-emergent events above grade 1. In exploratory analyses, ixazomib treatment was associated with detectable viremia that was below the lower limit of quantification (LLQ) in 9 participants, and viremia that was above LLQ in 4 of 16 participants. While treatment was associated with reduced CD4 counts [baseline 783 cells/ mm3 vs. week-24 724 cells/ mm3 p=0.003], there were no changes in markers of cellular activation, exhaustion or inflammation. Total HIV DNA and proviral sequencing were not altered by ixazomib treatment. Intact proviral DNA assay (IPDA) identified intact proviruses in 14 patients pre-treatment, and in 10/14 of those subjects post treatment values were reduced (P=0.068), allowing a calculated intact proviral half life of 0.6 years (95% CI 0.3, 2.5), compared to 7.1 years (95% CI 3.9, 18, p=0.004) in historical controls. Differentiation Quantitative Viral Outgrowth Assays (dQVOA) identified measurable proviruses in 15 subjects pre-treatment; post-treatment values were numerically reduced in 9, but overall differences were not significantly different. Interpretation Our study successfully met its primary endpoint of demonstrating the safety of ixazomib for 24 weeks in HIV infected persons. Exploratory analyses suggest that the effects observed ex vivo of latency reversal and reductions in HIV reservoir size, also occur in vivo. Future controlled studies of ixazomib are warranted. Funding This study was funded by Millennium Pharmaceuticals Inc..; the Mayo Clinic Foundation; the National Institutes of Health, including the National Institute of Allergy and Infectious Diseases, Division of AIDS, the National Heart, Lung and Blood Institute, the National Institute of Diabetes and Digestive and Kidney Diseases, the National Institute of Neurological Disorders and Stroke, and the National Institute on Drug Abuse. Mayo Clinic also acknowledges generous funding support from Mr. Joseph T. and Mrs. Michele P. Betten.

OBJECTIVE Tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) dependent apoptosis has been implicated in CD4 T-cell death and immunologic control of HIV-1 infection. We have described a splice variant called TRAILshort, which is a dominant negative ligand that antagonizes TRAIL-induced cell death in the context of HIV-1 infection. HIV-1 elite controllers naturally control viral replication for largely unknown reasons. Since enhanced death of infected cells might be responsible, as might occur in situations of low (or inhibited) TRAILshort, we tested whether there was an association between elite controller status and reduced levels of TRAILshort expression. DESIGN Cohort study comparing TRAILshort and full length TRAIL expression between HIV-1 elite controllers and viremic progressors from two independent populations. METHODS TRAILshort and TRAIL gene expression in peripheral blood mononuclear cells (PBMCs) was determined by RNA-seq. TRAILshort and TRAIL protein expression in plasma was determined by antibody bead array and proximity extension assay respectively. RESULTS HIV-1 elite controllers expressed less TRAILshort transcripts in PBMCs (P = 0.002) and less TRAILshort protein in plasma (P

Tumor mutation burden (TMB) is used to select patients to receive immune checkpoint inhibitors (ICIs) but has mixed predictive capabilities. We hypothesized that inactivation of antigen presenting genes (APGs) that result from increased TMBs would result in inherent resistance to ICIs. We observed that somatic mutations in APGs were associated with increasing TMBs across 9,418 tumor samples of 33 different histological subtypes. In adenocarcinomas of the lung, ITGAX and CD1B were some of the most commonly mutated APGs. In 62 patients with non-small cell lung cancers treated with a PD-1 inhibitor in second or later lines of therapy, there was an association of increased TMB with mutations in APGs; however, mutations in one or more APGs were associated with improved progression-free survival. Contrary to our hypothesis, mutations in APGs were associated with improved progression-free survival with nivolumab, possibly due to the involvement of single alleles rather than complete loss.

Nearly 150 million doses of FDA-authorized COVID vaccines have been administered in the United States. Sex-based differences of adverse events remain poorly understood, mandating the need for real-world investigation from Electronic Health Records (EHRs) and broader epidemiological data sets. Based on an augmented curation of EHR clinical notes of 31,064 COVID-vaccinated individuals (19,321 females and 11,743 males) in the Mayo Clinic, we find that nausea and vomiting were documented significantly more frequently in females than males after both vaccine doses (nausea: RRDose 1 = 1.67, pDose 1

Technology to generate single cell RNA-sequencing (scRNA-seq) datasets and tools to annotate them have rapidly advanced in the past several years. Such tools generally rely on existing transcriptomic datasets or curated databases of cell type defining genes, while the application of scalable natural language processing (NLP) methods to enhance analysis workflows has not been adequately explored. Here we deployed an NLP framework to objectively quantify associations between a comprehensive set of over 20,000 human protein-coding genes and over 500 cell type terms across over 26 million biomedical documents. The resultant gene-cell type associations (GCAs) are significantly stronger between a curated set of matched cell type-marker pairs than the complementary set of mismatched pairs (Mann Whitney p < 6.15×10−76, r = 0.24; cohen’s D = 2.6). Building on this, we developed an augmented annotation algorithm that leverages GCAs to categorize cell clusters identified in scRNA-seq datasets, and we tested its ability to predict the cellular identity of 185 clusters in 13 datasets from human blood, pancreas, lung, liver, kidney, retina, and placenta. With the optimized settings, the true cellular identity matched the top prediction in 66% of tested clusters and was present among the top five predictions for 94% of clusters. Further, contextualization of differential expression analyses with these GCAs highlights poorly characterized markers of established cell types, such as CLIC6 and DNASE1L3 in retinal pigment epithelial cells and endothelial cells, respectively. Taken together, this study illustrates for the first time how the systematic application of a literature derived knowledge graph can expedite and enhance the annotation and interpretation of scRNA-seq data.

The COVID-19 pandemic demands assimilation of all biomedical knowledge to decode mechanisms of pathogenesis. Despite the recent renaissance in neural networks, a platform for the real-time synthesis of the exponentially growing biomedical literature and deep omics insights is unavailable. Here, we present the nferX platform for dynamic inference from over 45 quadrillion possible conceptual associations from unstructured text, and triangulation with insights from single-cell RNA-sequencing, bulk RNA-seq and proteomics from diverse tissue types. A hypothesis-free profiling of ACE2 suggests tongue keratinocytes, olfactory epithelial cells, airway club cells and respiratory ciliated cells as potential reservoirs of the SARS-CoV-2 receptor. We find the gut as the putative hotspot of COVID-19, where a maturation correlated transcriptional signature is shared in small intestine enterocytes among coronavirus receptors (ACE2, DPP4, ANPEP). A holistic data science platform triangulating insights from structured and unstructured data holds potential for accelerating the generation of impactful biological insights and hypotheses.

The COVID-19 pandemic demands assimilation of all available biomedical knowledge to decode its mechanisms of pathogenicity and transmission. Despite the recent renaissance in unsupervised neural networks for decoding unstructured natural languages, a platform for the real-time synthesis of the exponentially growing biomedical literature and its comprehensive triangulation with deep omic insights is not available. Here, we present the nferX platform for dynamic inference from over 45 quadrillion possible conceptual associations extracted from unstructured biomedical text, and their triangulation with Single Cell RNA-sequencing based insights from over 25 tissues. Using this platform, we identify intersections between the pathologic manifestations of COVID-19 and the comprehensive expression profile of the SARS-CoV-2 receptor ACE2. We find that tongue keratinocytes and olfactory epithelial cells are likely under-appreciated targets of SARS-CoV-2 infection, correlating with reported loss of sense of taste and smell as early indicators of COVID-19 infection, including in otherwise asymptomatic patients. Airway club cells, ciliated cells and type II pneumocytes in the lung, and enterocytes of the gut also express ACE2. This study demonstrates how a holistic data science platform can leverage unprecedented quantities of structured and unstructured publicly available data to accelerate the generation of impactful biological insights and hypotheses., 5 figures

Purpose: TNF-related apoptosis inducing ligand (TRAIL) expression by immune cells contributes to antitumor immunity. A naturally occurring splice variant of TRAIL, called TRAILshort, antagonizes TRAIL-dependent cell killing. It is unknown whether tumor cells express TRAILshort and if it impacts antitumor immunity. Experimental Design: We used an unbiased informatics approach to identify TRAILshort expression in primary human cancers, and validated those results with IHC and ISH. TRAILshort-specific mAbs were used to determine the effect of TRAILshort on tumor cell sensitivity to TRAIL, and to immune effector cell dependent killing of autologous primary tumors. Results: As many as 40% of primary human tumors express TRAILshort by both RNA sequencing and IHC analysis. By ISH, TRAILshort expression is present in tumor cells and not bystander cells. TRAILshort inhibition enhances cancer cell lines sensitivity to TRAIL-dependent killing both in vitro and in immunodeficient xenograft mouse models. Immune effector cells isolated from patients with B-cell malignancies killed more autologous tumor cells in the presence compared with the absence of TRAILshort antibody (P < 0.05). Conclusions: These results identify TRAILshort in primary human malignancies, and suggest that TRAILshort blockade can augment the effector function of autologous immune effector cells. See related commentary by de Miguel and Pardo, p. 5546

The recent explosion of biomedical knowledge presents both a major opportunity and challenge for scientists tackling complex problems in healthcare. Here we present an approach for synthesizing biomedical knowledge based on a combination of word-embeddings and select cooccurrences. We evaluated our ability to recapitulate and retrospectively predict disease-gene associations from the Online Mendelian Inheritance in Man (OMIM) resource. Our metrics achieved an area under the curve (AUC) value of 0.981 at the recapitulation task for 2,400 disease-gene associations. At the most stringent cutoff, our metrics predicted 13.89% of these associations before their first cooccurrence in the literature, with a median time of 4 years between prediction and first cooccurrence. Finally, our literature metrics can be combined with human genetics data to retrospectively predict disease-gene associations, IL-6 and Giant Cell Arteritis provided as an example. We believe this framework can provide robust biomedical hypotheses at a much faster pace than current standard practices.

Introduction Malignant pleural mesothelioma is a disease primarily associated with exposure to the carcinogen asbestos. Whereas other carcinogen-related tumors are associated with a high tumor mutation burden, mesothelioma is not. We sought to resolve this discrepancy. Methods We used mate-pair (n = 22), RNA (n = 28), and T cell receptor sequencing along with in silico predictions and immunologic assays to understand how structural variants of chromosomes affect the transcriptome. Results We observed that inter- or intrachromosomal rearrangements were present in every specimen and were frequently in a pattern of chromoanagenesis such as chromoplexy or chromothripsis. Transcription of rearrangement-related junctions was predicted to result in many potential neoantigens, some of which were proven to bind patient-specific major histocompatibility complex molecules and to expand intratumoral T cell clones. T cells responsive to these predicted neoantigens were also present in a patient's circulating T cell repertoire. Analysis of genomic array data from the mesothelioma cohort in The Cancer Genome Atlas suggested that multiple chromothriptic-like events negatively impact survival. Conclusions Our findings represent the discovery of potential neoantigen expression driven by structural chromosomal rearrangements. These results may have implications for the development of novel immunotherapeutic strategies and the selection of patients to receive immunotherapies.

Synovial sarcoma (SS) is defined by the hallmark SS18-SSX fusion oncoprotein, which renders BAF complexes aberrant in two manners: gain of SSX to the SS18 subunit and concomitant loss of BAF47 subunit assembly. Here we demonstrate that SS18-SSX globally hijacks BAF complexes on chromatin to activate an SS transcriptional signature that we define using primary tumors and cell lines. Specifically, SS18-SSX retargets BAF complexes from enhancers to broad polycomb domains to oppose PRC2-mediated repression and activate bivalent genes. Upon suppression of SS18-SSX, reassembly of BAF47 restores enhancer activation, but is not required for proliferative arrest. These results establish a global hijacking mechanism for SS18-SSX on chromatin, and define the distinct contributions of two concurrent BAF complex perturbations.

Current unbiased approaches to mine the large amounts of patient-level data on mutations, structural variations and gene expression result in an unwieldy amount of interactions and correlations, which cannot be parsed to identify disease drivers. Here we present an approach to encode mutational and structural variant data at a patient level in a semantic association space. This approach transforms the presence of a mutation (or other feature) in each patient into the quantitative semantic association score of the corresponding gene and the phenotype of interest, which we have trained on all publicly available literature using word-embedding neural networks. Using data from The Cancer Genome Atlas (TCGA), we encoded the mutation or structural variant status (incl. copy number, fusion and chromothripsis) of all patients in the Lung Adenocarcinoma and Mesothelioma cohorts into our semantic space. For each cancer, we first defined the set of genes that are most associated to it according to the literature. To project each patient into this semantic space, we next determined if each patient had a mutation in the genes representing the disease semantic vector (e.g. NSCLC). For TCGA data we only counted non-Silent mutations and represented them as a binary number for each gene, i.e. 0 if the patient had no mutations in that gene and 1 if the patient had a non-Silent mutation in the gene. Each patient was then encoded in a binary vector with each member corresponding to a gene from the disease semantic vector. For example, lung adenocarcinoma was associated to 1,367 genes in our semantic space. A lung adenocarcinoma patient’s vector would them be composed of 1,367 binary numbers dictating if the gene is mutated or not in that patient. We then multiply these binary vectors with the semantic disease vector to obtain the patient’s projection in the disease space, which in effect replaces the binary number with the Semantic Association Score between the gene and the disease. Contrary to clustering patient samples by their mutation or structural variant data alone, our projected patient vectors clustered patients together into 22 groups with high patient-to-patient similarity. These clusters recapitulate canonical knowledge about the disease, e.g. Lung Adenocarcinoma patients form clusters that include EGFR-driven and KRAS-driven cohorts. We also see novel groups of patients driven by genes such as MET, STK11 and MALAT1. These clusters can be further stratified by their survival status and other clinical features. We validated our approach with a non-TCGA Mesothelioma cohort, revealing similarities in patient stratification regardless of the data source. This approach represents a dramatic shift in patient segmentation, delivering real-time grouping of patients and biomarker identification, which can accelerate clinical trial design and therapeutic development strategy. Citation Format: Enrique Garcia-Rivera, Aaron S. Mansfield, Karthik Murugadoss, Murali Aravamudan. Patient segmentation using machine-learning based literature and genomic data synthesis uncovers novel cohorts of NSCLC and mesothelioma patients [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 2452.