Responder and non-responder
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ABSTRACT:
PROVIDER: PRJEB61041 | ENA |
REPOSITORIES: ENA
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Project description:Purpose: To evaluate the presence of a gene expression signature present before treatment as predictive of response to treatment with MAGEâA3 immunotherapeutic in metastatic melanoma patients and to validate its predictivity in adjuvant therapy of early-stage lung cancer. Patients were participants in two Phase II studies of the recombinant MAGEâA3 antigen combined with immunological adjuvants. mRNA from tumor samples (biopsies) collected before MAGE-A3 immunotherapy was analyzed by microarray hybridization and by quantitative polymerase chain reaction (qRT-PCR). The melanoma microarray dataset was used to discover and crossvalidate a gene expression signature and classifier discriminative of Responders (R) versus Non-Responders (NR) patients; the gene signature and classifier were then applied to an adjuvant lung cancer study. Patients that were not included for analysis are denoted as NE (Non-evaluable). GSK Biologicals
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Project description:The therapeutic efficacy of cancer vaccines can be optimized from 3 aspects: adjuvants, vaccine formulation, and processing of tumor antigens. Herein, various different adjuvants, such as toll like receptors and STING agonists etc., and their combinations were compared in cancer nanovaccines loaded with whole tumor antigens were illustrated. By comparing different sized nanovaccines and micronvaccines, 200nm-400nm and 2.5μm were discovered to be optimal sizes of nanovaccines and micronvaccines. Rapid freezing, fixation, heating, salting-out, ethanol precipitate can affect the immunogenicity of tumor antigens and efficacy of cancer vaccines. The optimal cancer vaccine can cure all or most tumor-bearing body in melanoma mouse model, lung cancer mouse model, subcutaneous pancreatic cancer model, orthotopic pancreatic cancer model, melanoma lung metastasis model. In addition, the immune cell profiles were systematically investigated using single-cell sequencing, in blood, splenocytes and draining lymph nodes of healthy mice, PBS treated tumor-bearing mice, vaccine-treated non-cured mice and cured mice after different time. By comparing 21 samples, some featured clusters and markers, such as S100A4, KLRG1, SIPR5, CXCR3, IL2Ra, IKZF2, CX3CR1, S100A8, and S100A9 etc., were identified to distinguish responders and non-responders to immunotherapy. These biomarkers were verified and confirmed the feasibility of predicting therapeutic efficacy in mouse cancer model and cancer patients. In summary, this study presented a method to optimize cancer nanovacines from different aspects, systematically investigated immune cell profiles in non-responders and responders to immunotherapy, and discovered some featured biomarkers for predicting or distinguishing responders and non-responders to immunotherapy.
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2017-12-20 | E-MTAB-6214 | biostudies-arrayexpress
Project description:There is a strong correlation between myeloid derived suppressor cells (MDSCs) and resistance to immune checkpoint blockade (ICB), but the detailed underlying this correlation are largely unknown. Using single-cell RNA-seq analysis in a bilateral tumor model, we found that immunosuppressive myeloid cells with characteristics of fatty acid oxidative metabolism dominate the immune-cell landscape in ICB-resistant subjects. In addition, we uncovered a previously underappreciated role of a serine/threonine kinase, PIM1, in regulating lipid oxidative metabolism via PPARγ-mediated activities. Enforced PPARγ expression sufficiently rescued metabolic and functional defects of Pim1-/- MDSCs. Consistent with this, pharmacological inhibition of PIM kinase by AZD1208 treatment significantly disrupted myeloid cell–mediated immunosuppression microenvironment and unleashed CD8+ T cell–mediated antitumor immunity, which enhanced PD-L1 blockade in preclinical cancer models. PIM kinase inhibition also sensitized non-responders to PD-L1 blockade by selectively targeting suppressive myeloid cells. Overall, we have identified PIM1 as a metabolic modulator in MDSCs that is associated with ICB resistance and can be therapeutically targeted to overcome ICB resistance.
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Project description:Immunotherapy, such as anti-PD1, has improved the survival of patients with metastatic melanoma; However, predicting which patients will respond to immunotherapy is still unknown. In this study we analyzed pre-immunotherapy treated tumors from 52 patients with metastatic melanoma and monitored their response based on RECIST 1.1 criteria. The responders group contained 21 patients that had a complete or partial response, while the 31 non-responders had stable or progressive disease. Whole exome sequencing (WES) was used to identify biomarkers of anti-PD1 response from somatic mutations between the two groups. Variants in codons G34 and G41 in NFKBIE, a negative regulator of NF-kB, were found exclusively in the responders. NKBIE-related genes within the responder group were also enriched compared to the non-responders. Patients that harbored NFKBIE-related gene mutations also had a higher mutational burden, decreased tumor volume with treatment, and increased progression-free survival. RNA sequencing on a subsection of tumor samples identified differential expression of the TNFA signaling via NFKB pathway, which includes CD83. By overexpressing NFKBIEG34E we were able to demonstrate this mutation is related to increased NF-kB activity, including increased CD83 protein expression when compared with the wildtype. These results suggest that increased NF-kB signaling as a consequence of an NFKBIE mutation may contribute to a favorable anti-PD1 treatment response, including a possible novel role of CD83 in solid tumors.
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