<script type="text/javascript">
<!--
document.write('<div id="oa_widget"></div>');
document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=undefined&type=result"></script>');
-->
</script>
Background It is now well established that the immune system has a substantial role in controlling cancer growth and progression. Immunotherapy is quickly coming to the forefront of cancer treatment,however the implementation of immunotherapy in pediatric solid cancers, which classically display a low mutational load, is hindered by insufficient understanding of the determinants of cancer immune responsiveness in children. In order to better understand tumor-host interplay, we sought to characterize solid pediatric cancers based on immunological parameters(1)using analytes extracted from gene expression data. Methods We performed single sample GeneSet Enrichment Analysis for 105 immune signatures previously described on 5pediatric tumors (410patients) from TARGET dataset(1) to identify coherent signature modules. Then we clustered samples according to representative signatures (1)and compared survival across clusters. We completed the analysis by analyzing the enrichment of immune subpopulations and the expression of the immune checkpoints. The degree of dysregulation of oncogenic pathways was also assessed. The performance of previously identified immune signatures as the Immunologic Constant of Rejection(2,3),which captures an active Th1/cytotoxic response associated with favorable prognosis and responsiveness to immunotherapy, was also checked within each tumor subtype. Results We found 5main modules, in agreement with results obtained in adult solid tumors:Wound Healing,TGF-B signaling,IFN-G signaling, Macrophages and Lymphocytes (figure 1). These 5 modules clustered pediatric patients into 6 immune subtypes S1-S6 with distinct survival (S2vsS4,p=0.0044, adjusted for cancer type),S2 cluster has the best overall survival and characterized by low enrichment of wound healing signature, high Th1, low Th2 and high expression of HLA 1 and HLA2, while the opposite holds true for cluster S4 with the worst survival and highest enrichment of wound healing signature, high Th2, and low Th1.The S6 cluster is characterized by highest enrichment of lymphocyte signature, the highest expression of immune checkpoints accompanied by elevated expression of exhaustion markers, and an unpolarized immune response with high abundance of macrophages. Additionally, pan-cancer, the upregulation of WNT-Beta catenin pathway is associated with adverse outcome and lack of T-cell infiltration. In the per-cancer analysis, ICR is associated with better survival in osteosarcoma and with worse survival in Wilms’ tumors,similarly with what observed in adult kidney’s cancer despite the different embryological origin. Conclusions We demonstrated that pediatric solid cancers can be classified according to their immune disposition, unveiling unexpected similarity with adults’ tumors.Immunological parameters might be explored to refine diagnostic and prognostic biomarkers and to identify potential immune responsive tumors. This is the first pan-cancer immunogenomic analysis in children. References Thorsson V, Gibbs DL, Brown SD, et al. The immune landscape of cancer. Immunity 2018. 48(4):812–830. Roelands J, Hendrickx W, et al. ‘Oncogenic states dictate the prognostic and predictive connotations of intratumoral immune response.’ Journal for immunotherapy of cancer 2020;vol. 8:1. Galon J, Angell HK, Bedognetti D, Marincola FM. The continuum of cancer immunosurveillance: prognostic, predictive, and mechanistic signatures. Immunity 2013;39:11–26.
Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |