Faculty Bibliography

Metastasis is the primary cause of death of cancer patients. Dissecting mechanisms governing metastatic spread may uncover important tumor biology and/or yield promising therapeutic insights. Here, we investigated the role of circular RNAs (circRNA) in metastasis, using melanoma as a model aggressive tumor. We identified silencing of cerebellar degeneration-related 1 antisense (CDR1as), a regulator of miR-7, as a hallmark of melanoma progression. CDR1as depletion results from epigenetic silencing of LINC00632, its originating long non-coding RNA (lncRNA) and promotes invasion in vitro and metastasis in vivo through a miR-7-independent, IGF2BP3-mediated mechanism. Moreover, CDR1as levels reflect cellular states associated with distinct therapeutic responses. Our study reveals functional, prognostic, and predictive roles for CDR1as and expose circRNAs as key players in metastasis.

Regulatory T (T_reg) cells accumulate into tumors, hindering the success of cancer immunotherapy. Yet, therapeutic targeting of T_reg cells shows limited efficacy or leads to autoimmunity. The molecular mechanisms that guide T_reg cell stability in tumors remain elusive. In the present study, we identify a cell-intrinsic role of the alarmin interleukin (IL)-33 in the functional stability of T_reg cells. Specifically, IL-33-deficient T_reg cells demonstrated attenuated suppressive properties in vivo and facilitated tumor regression in a suppression of tumorigenicity 2 receptor (ST2) (IL-33 receptor)-independent fashion. On activation, Il33^-/- T_reg cells exhibited epigenetic re-programming with increased chromatin accessibility of the Ifng locus, leading to elevated interferon (IFN)-γ production in a nuclear factor (NF)-κB-T-bet-dependent manner. IFN-γ was essential for T_reg cell defective function because its ablation restored Il33^-/- T_reg cell-suppressive properties. Importantly, genetic ablation of Il33 potentiated the therapeutic effect of immunotherapy. Our findings reveal a new and therapeutically important intrinsic role of IL-33 in T_reg cell stability in cancer.

Despite substantial progress in lung cancer immunotherapy, the overall response rate in KRAS-mutant lung adenocarcinoma (ADC) patients remains low. Combining standard immunotherapy with adjuvant approaches that enhance adaptive immune responses-such as epigenetic modulation of anti-tumor immunity-is therefore an attractive strategy. To identify epigenetic regulators of tumor immunity, we constructed an epigenetic-focused sgRNA library, and performed an in vivo CRISPR screen in a KrasG12D/P53-/- (KP) lung ADC model. Our data showed that loss of the histone chaperone Asf1a in tumor cells sensitizes tumors to anti-PD-1 treatment. Mechanistic studies revealed that tumor cell-intrinsic Asf1a deficiency induced immunogenic macrophage differentiation in the tumor microenvironment by upregulating GM-CSF expression and potentiated T cell activation in combination with anti-PD-1. Our results provide rationale for a novel combination therapy consisting of ASF1A inhibition and anti-PD-1 immunotherapy.

DISCLOSURES/UNASSIGNED:Melnick: Janssen: Research Funding; Epizyme: Consultancy; Constellation: Consultancy.

General control nonderepressible 2 (GCN2) is an environmental sensor controlling transcription and translation in response to nutrient availability. Although GCN2 is a putative therapeutic target for immuno-oncology, its role in shaping the immune response to tumors is poorly understood. Here, we used mass cytometry, transcriptomics, and transcription factor-binding analysis to determine the functional impact of GCN2 on the myeloid phenotype and immune responses in melanoma. We found that myeloid-lineage deletion of GCN2 drives a shift in the phenotype of tumor-associated macrophages and myeloid-derived suppressor cells (MDSCs) that promotes antitumor immunity. Time-of-flight mass cytometry (CyTOF) and single-cell RNA sequencing showed that this was due to changes in the immune microenvironment with increased proinflammatory activation of macrophages and MDSCs and interferon-γ expression in intratumoral CD8⁺ T cells. Mechanistically, GCN2 altered myeloid function by promoting increased translation of the transcription factor CREB-2/ATF4, which was required for maturation and polarization of macrophages and MDSCs in both mice and humans, whereas targeting Atf4 by small interfering RNA knockdown reduced tumor growth. Last, analysis of patients with cutaneous melanoma showed that GCN2-dependent transcriptional signatures correlated with macrophage polarization, T cell infiltrates, and overall survival. Thus, these data reveal a previously unknown dependence of tumors on myeloid GCN2 signals for protection from immune attack.

Local translation can support memory consolidation by supplying new proteins to synapses undergoing plasticity. Translation in adult forebrain dendrites is an established mechanism of synaptic plasticity and is regulated by learning, yet there is no evidence for learning-regulated protein synthesis in adult forebrain axons, which have traditionally been believed to be incapable of translation. Here we show that axons in the adult rat amygdala contain translation machinery, and use translating ribosome affinity purification (TRAP) with RNASeq to identify mRNAs in cortical axons projecting to the amygdala, over 1200 of which were regulated during consolidation of associative memory. Mitochondrial and translation-related genes were upregulated, whereas synaptic, cytoskeletal, and myelin-related genes were downregulated; the opposite effects were observed in the cortex. Our results demonstrate that axonal translation occurs in the adult forebrain and is altered after learning, supporting the likelihood that local translation is more a rule than an exception in neuronal processes.

A major challenge in cancer treatment is predicting clinical response to anti-cancer drugs on a personalized basis. Using a pharmacogenomics database of 1,001 cancer cell lines, we trained deep neural networks for prediction of drug response and assessed their performance on multiple clinical cohorts. We demonstrate that deep neural networks outperform the current state in machine learning frameworks. We provide a proof of concept for the use of deep neural network-based frameworks to aid precision oncology strategies.