G. Unger, B. Wuertz, C. L. Pruett, M. Watkins, P. Gaffney, F. Ondrey
{"title":"Abstract 2590: Genomic analysis points to fibrosis and changes in fat metabolism in oral cancer progression","authors":"G. Unger, B. Wuertz, C. L. Pruett, M. Watkins, P. Gaffney, F. Ondrey","doi":"10.1158/1538-7445.AM2021-2590","DOIUrl":null,"url":null,"abstract":"Head and neck cancer long term survival has only experienced marginal gains over the past 3 decades. Further, there is inadequate understanding of the biology of carcinogenesis and recurrence, as well as its relation to the microenvironment. Deeper understanding of these areas would provide improved molecular target identification. In pursuit of this goal, a small clinical trial collected lesion and adjacent normal-appearing mucosa for subsequent RNA-seq analysis. Patients were grouped by post-surgical pathology as either precancer (hyperplasia - severe dysplasia) or cancer (carcinoma in situ - early stage invasive cancer). Following identification of differentially expressed (DE) genes, DE genesets were submitted for Ingenuity Pathway Analysis (IPA). Hierarchical clustering illustrates distinct separation between lesion and perilesional normal mucosa of the top 100 DE genes. Among the top 25 dysregulated pathways, 50% were associated with creation of fibrotic tumor microenvironment (TME), 20% were related to changes in immune populations inhabiting the TME and 10% devoted to metabolism changes. Subgroup analysis, (precancer vs. cancer), revealed dysregulation of metabolism (~50%) predominating in precancer. Metabolism remained an important dysregulation at 30% of the top 25 pathways in cancer. Protein network analysis, (Metascape on-line tool), confirmed IPA results, illustrating an extensive, previously undescribed, interconnectedness of fibrosis with shifts in fatty acid metabolism from oxidative to gluconeogenesis, providing a foundation for choosing targets amenable to cancer prevention. Several notable pathways are likely contributed to by inflammatory and other cells in the milieu, not precancer cells themselves. So, we dove deeper, using EpIC (Epitope Immunogenicity Characterization) algorithm to assess relative percentages of non-tumor cells based on 20-count gene signatures. Gene expression favored a profile of significantly increasing cancer-associated fibroblasts, decreasing CD-8 killer T cells, and increasing vascular endothelial cells during progression, with macrophage content slightly increasing in cancer specimens. These findings suggest interaction between immunoinflammatory milieu and precancerous cells promoting malignancy. Several high yield target pathways are related to published mechanisms of action for drugs of high interest to our cancer prevention program (pioglitazone/metformin). Further, we confirmed protein network analysis in an additional oral carcinoma dataset from Conway et. al, (Oncotarget 2015). Citation Format: Gretchen M. Unger, Beverly R. Wuertz, Charles L. Pruett, Matthew Watkins, Patrick M. Gaffney, Frank G. Ondrey. Genomic analysis points to fibrosis and changes in fat metabolism in oral cancer progression [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2590.","PeriodicalId":20290,"journal":{"name":"Prevention Research","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Prevention Research","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1158/1538-7445.AM2021-2590","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Head and neck cancer long term survival has only experienced marginal gains over the past 3 decades. Further, there is inadequate understanding of the biology of carcinogenesis and recurrence, as well as its relation to the microenvironment. Deeper understanding of these areas would provide improved molecular target identification. In pursuit of this goal, a small clinical trial collected lesion and adjacent normal-appearing mucosa for subsequent RNA-seq analysis. Patients were grouped by post-surgical pathology as either precancer (hyperplasia - severe dysplasia) or cancer (carcinoma in situ - early stage invasive cancer). Following identification of differentially expressed (DE) genes, DE genesets were submitted for Ingenuity Pathway Analysis (IPA). Hierarchical clustering illustrates distinct separation between lesion and perilesional normal mucosa of the top 100 DE genes. Among the top 25 dysregulated pathways, 50% were associated with creation of fibrotic tumor microenvironment (TME), 20% were related to changes in immune populations inhabiting the TME and 10% devoted to metabolism changes. Subgroup analysis, (precancer vs. cancer), revealed dysregulation of metabolism (~50%) predominating in precancer. Metabolism remained an important dysregulation at 30% of the top 25 pathways in cancer. Protein network analysis, (Metascape on-line tool), confirmed IPA results, illustrating an extensive, previously undescribed, interconnectedness of fibrosis with shifts in fatty acid metabolism from oxidative to gluconeogenesis, providing a foundation for choosing targets amenable to cancer prevention. Several notable pathways are likely contributed to by inflammatory and other cells in the milieu, not precancer cells themselves. So, we dove deeper, using EpIC (Epitope Immunogenicity Characterization) algorithm to assess relative percentages of non-tumor cells based on 20-count gene signatures. Gene expression favored a profile of significantly increasing cancer-associated fibroblasts, decreasing CD-8 killer T cells, and increasing vascular endothelial cells during progression, with macrophage content slightly increasing in cancer specimens. These findings suggest interaction between immunoinflammatory milieu and precancerous cells promoting malignancy. Several high yield target pathways are related to published mechanisms of action for drugs of high interest to our cancer prevention program (pioglitazone/metformin). Further, we confirmed protein network analysis in an additional oral carcinoma dataset from Conway et. al, (Oncotarget 2015). Citation Format: Gretchen M. Unger, Beverly R. Wuertz, Charles L. Pruett, Matthew Watkins, Patrick M. Gaffney, Frank G. Ondrey. Genomic analysis points to fibrosis and changes in fat metabolism in oral cancer progression [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2590.
头颈癌的长期生存率在过去的30年里只有很小的提高。此外,人们对癌变和复发的生物学及其与微环境的关系了解不足。深入了解这些领域将有助于改进分子靶标识别。为了实现这一目标,一项小型临床试验收集了病变和邻近正常粘膜,用于随后的RNA-seq分析。患者根据术后病理分为癌前病变(增生-严重不典型增生)或癌(原位癌-早期浸润性癌)。鉴定差异表达(DE)基因后,将DE基因集提交独创性途径分析(Ingenuity Pathway Analysis, IPA)。分级聚类显示前100个DE基因在病变和病变周围正常粘膜之间有明显的分离。在前25个失调通路中,50%与纤维化肿瘤微环境(TME)的产生有关,20%与居住在TME中的免疫群体的变化有关,10%与代谢变化有关。亚组分析(癌前病变vs癌前病变)显示代谢失调(约50%)主要发生在癌前病变。在癌症的前25个途径中,30%的代谢仍然是一个重要的失调。蛋白质网络分析(metscape在线工具)证实了IPA结果,说明了先前未描述的纤维化与脂肪酸代谢从氧化到糖异生的转变之间的广泛相互联系,为选择适合癌症预防的靶点提供了基础。一些值得注意的途径可能是由环境中的炎症细胞和其他细胞促成的,而不是癌前细胞本身。因此,我们深入研究,使用EpIC(表位免疫原性表征)算法来评估基于20计数基因签名的非肿瘤细胞的相对百分比。基因表达倾向于癌症相关成纤维细胞显著增加,CD-8杀伤T细胞减少,血管内皮细胞增加,在癌症标本中巨噬细胞含量略有增加。这些发现提示免疫炎症环境与促进恶性肿瘤的癌前细胞之间存在相互作用。一些高产靶标途径与癌症预防项目(吡格列酮/二甲双胍)中已发表的药物的作用机制有关。此外,我们在Conway等人的另一个口腔癌数据集中证实了蛋白质网络分析(Oncotarget 2015)。引文格式:Gretchen M. Unger, Beverly R. Wuertz, Charles L. Pruett, Matthew Watkins, Patrick M. Gaffney, Frank G. Ondrey。基因组分析指出了口腔癌进展过程中纤维化和脂肪代谢的变化[摘要]。见:美国癌症研究协会2021年年会论文集;2021年4月10日至15日和5月17日至21日。费城(PA): AACR;癌症杂志,2021;81(13 -增刊):2590。