Garcia-Rivera, Enrique

TRAIL is a TNF family ligand that trimerizes TRAIL-R1 (DR4) or TRAIL-R2 (DR5) to induce apoptosis, necroptosis, and/or NF-κB activation in receptor-bearing cells. We previously identified TRAILshort as a splice variant of TRAIL that lacks cysteine 230, cannot trimerize, and acts as a dominant-negative ligand that blocks TRAIL-mediated apoptosis. TRAILshort is expressed on cell surfaces and within extracellular vesicles, enabling it to confer TRAIL resistance to both producing and bystander cells. In this study, we showed that elevated TRAILshort levels were associated with chronic viral infections, cancer, and autoimmune diseases, suggesting a link to impaired immune regulation. Using unbiased phosphoproteomics and mechanistic studies, we demonstrated that TRAILshort binding to DR5 recruited and activated the phosphatase Src homology region 2 domain–containing phosphatase 1 (SHP-1), leading to zeta-chain-associated protein kinase 70 (ZAP-70) dephosphorylation, disruption of ZAP-70–CD3ζ interactions, and impaired T cell receptor signaling, thereby reducing T cell activation, proliferation, and cytokine production in response to antigen or CD3/CD28 ligation. Genetic or pharmacologic SHP-1 inhibition reverses these effects. In humanized mouse models, TRAILshort promoted the persistence of transformed mouse embryonic fibroblasts (MEFs) and L428 and antagonized CD19-directed CAR T cell activity, revealing TRAILshort as an immunomodulator of T cell function with therapeutic implications, including blocking TRAILshort to restore T cell immunity or delivering TRAILshort to enforce tolerance. TRAILshort, a variant of TRAIL, suppresses T cell function and immune responses in cancer and infectious diseases, highlighting its potential as a therapeutic target.

Abstract 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

Abstract 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.

Abstract Chromothripsis is a complex genomic event characterized by a high number of chromosomal rearrangements and copy number changes that typically localize to a specific region on a single chromosome. Next generation sequencing has led to the initial discovery and description of these events which have since been observed in multiple human cancers. Whereas most mutagenic processes occur gradually, the multiple structural variations of each chromothriptic event likely occur at once. The clinical significance of chromothripsis remains uncertain, so we sought to characterize the relationship between chromothripsis and survival across human malignancies. We analyzed 13,318 tumors of 36 types using publicly available data from The Cancer Genome Atlas with CTLPScanner, which uses a sliding window scan statistic to identify events consistent with a chromothripsis-like pattern (CTLP). Our cutoffs for CTLP of copy number variation ≥ 20 and a log10 likelihood ratio ≥ 8 were derived from a clinical training set previously described by CTLPScanner. We detected one or more events consistent with a CTLP in 8,276 tumors (62.1%) for a total 24,879 events. There were no events in 5,042 cases (37.86%), a significant decrease from established rates of CTLPs. CTLPs were more common in ovarian serous cystadenocarcinomas (89.8%), esophageal carcinomas (86.5%) and sarcomas (81.2%) than in thymomas (26.6%) and thyroid carcinomas (15.2%). Chromosomes 8, 1 and 6 were most frequently involved with CTLPs. CTLPs in chromosome 6 were associated with uveal melanoma, chromophobic renal cell carcinoma and cholangiocarcinomas whereas CTLPs involving chromosome 12 were associated with testicular germ cell tumors. Multiple CTLPs were found in 5,262 tumors (39.5%, range of events 2-18+) and were most common in ovarian serous cystadenocarcinomas and sarcomas. The presence of CTLPs was associated with diminished overall survival across all tumor types (HR 1.5, p<2e-16). Given the potential for multiple CTLPs to have a greater effect on outcomes, we compared the survival of patients whose tumors contained 3 or more CTLPs (n=3,255, 24.4%) to those with 0-1 or 2 CTLPs. The presence of 3 or more CTLPs was associated with the worst survival amongst these groups (HR 1.67, p<2e-16). We identified that CTLPs are common in human cancers despite their prior descriptions as rare events. This discrepancy is likely due to our detection parameters. In addition, we identified that having multiple co-occurring CTLPs is associated with a worse prognosis, suggesting this type of event is more clinically relevant than a single CTLP and can be used as a negative prognostic marker in human malignancies. Citation Format: Aaron S. Mansfield, Enrique Garcia-Rivera. Chromothripsis and survival in human malignancies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2258.