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Immune response in lung cancer mouse model mimics human anti-Hu reactivity.

ABSTRACT: Most patients with paraneoplastic encephalomyelitis/sensory neuronopathy PEM/SN have small-cell lung cancer (SCLC) and develop antibodies against neuronal-specific Hu proteins, which are abnormally expressed in the tumor. Anti-Hu reactivity is present in ~16% of SCLC patients without PEM/SN. Here we test the hypothesis that engineered SCLC-prone mice may exhibit anti-Hu reactivity. We show that tumors from SCLC-prone mice misexpress Hu proteins, and 14% of mice harbor anti-Hu antibodies. Mice appear to show reactivity prior to clinical diagnosis of SCLC. This mouse model system will be useful to study SCLC-associated autoimmunity, its diagnostic value, and the potential protective role of oncoantigen-directed autoantibodies.

SUBMITTER: Kazarian M

PROVIDER: S-EPMC2788043 | biostudies-literature | 2009 Dec

REPOSITORIES: biostudies-literature

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Immune response in lung cancer mouse model mimics human anti-Hu reactivity.

Kazarian Meleeneh M Calbo Joaquim J Proost Natalie N Carpenter Catherine L CL Berns Anton A Laird-Offringa Ite A IA

Journal of neuroimmunology 20090917 1-2

Most patients with paraneoplastic encephalomyelitis/sensory neuronopathy PEM/SN have small-cell lung cancer (SCLC) and develop antibodies against neuronal-specific Hu proteins, which are abnormally expressed in the tumor. Anti-Hu reactivity is present in ~16% of SCLC patients without PEM/SN. Here we test the hypothesis that engineered SCLC-prone mice may exhibit anti-Hu reactivity. We show that tumors from SCLC-prone mice misexpress Hu proteins, and 14% of mice harbor anti-Hu antibodies. Mice ap ...[more]

PMID: 19765830

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Project description:BackgroundAdaptive immune resistance induces an immunosuppressive tumor environment that enables immune evasion. This phenomenon results in tumor escape with progression and metastasis. Programmed cell death-ligand 1 (PD-L1) expressed on tumors is thought to inhibit tumor-infiltrating lymphocytes (TILs) through programmed cell death 1 (PD1), enabling adaptive immune resistance. This study investigates the role of PD-L1 in both mouse and human neuroblastoma immunity. The consequence of PD-L1 inhibition is characterized in the context of an established whole tumor cell vaccine.Methods and findingsA mouse model of neuroblastoma was investigated using an Id2 knockdown whole cell vaccine in combination with checkpoint inhibition. We show that immunogenic mouse neuroblastoma acquires adaptive immune resistance by up-regulating PD-L1 expression, whereas PD-L1 is of lesser consequence in nonimmunogenic neuroblastoma tumors. Combining PD-L1 checkpoint inhibition with whole tumor cell/anti-CTLA-4 vaccination enhanced tumor cell killing, cured mice with established tumors, and induced long-term immune memory (6 months). From an evaluation of patient neuroblastoma tumors, we found that the inflammatory environment of the mouse neuroblastoma mimicked human disease in which PD-L1 expression was associated directly with TILs and lower-risk tumors. High-risk patient tumors were lacking both TILs and PD-L1 expression. Although a correlation in immunity seems to exist between the mouse model and human findings, the mouse tumor model is induced and not spontaneously occurring, and furthermore, the number of both mouse and human correlates is limited.ConclusionsThis study demonstrates the role PD-L1 plays in neuroblastoma's resistance to immunity and defines the nonredundant effect of combination checkpoint inhibition with vaccine therapy in a mouse model. High-risk, nonimmunogenic human tumors display both diminished PD-L1 expression and adaptive immune resistance. Paradoxically, high-risk tumors may be more responsive to effective vaccine therapy because of their apparent lack of adaptive immune resistance.

Project description:BackgroundBreast cancer is a complex and heterogeneous disease with distinct subtypes and molecular profiles corresponding to different clinical outcomes. Mouse models of breast cancer are widely used, but their relevance in capturing the heterogeneity of human disease is unclear. Previous studies have shown the heterogeneity at the gene expression level for the MMTV-Myc model, but have only speculated on the underlying genetics.MethodsTumors from the microacinar, squamous, and EMT histological subtypes of the MMTV-Myc mouse model of breast cancer underwent whole genome sequencing. The genomic data obtained were then integrated with previously obtained matched sample gene expression data and extended to additional samples of each histological subtype, totaling 42 gene expression samples. High correlation was observed between genetic copy number events and resulting gene expression by both Spearman's rank correlation coefficient and the Kendall rank correlation coefficient. These same genetic events are conserved in humans and are indicative of poor overall survival by Kaplan-Meier analysis. A supervised machine learning algorithm trained on METABRIC gene expression data was used to predict the analogous human breast cancer intrinsic subtype from mouse gene expression data.ResultsHerein, we examine three common histological subtypes of the MMTV-Myc model through whole genome sequencing and have integrated these results with gene expression data. Significantly, key genomic alterations driving cell signaling pathways were well conserved within histological subtypes. Genomic changes included frequent, co-occurring mutations in KIT and RARA in the microacinar histological subtype as well as SCRIB mutations in the EMT subtype. EMT tumors additionally displayed strong KRAS activation signatures downstream of genetic activating events primarily ascribed to KRAS activating mutations, but also FGFR2 amplification. Analogous genetic events in human breast cancer showed stark decreases in overall survival. In further analyzing transcriptional heterogeneity of the MMTV-Myc model, we report a supervised machine learning model that classifies MMTV-Myc histological subtypes and other mouse models as being representative of different human intrinsic breast cancer subtypes.ConclusionsWe conclude the well-established MMTV-Myc mouse model presents further opportunities for investigation of human breast cancer heterogeneity.

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Immune response in lung cancer mouse model mimics human anti-Hu reactivity.

Publications

Immune response in lung cancer mouse model mimics human anti-Hu reactivity.

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