摘要:基于蛋白质组学/基因和临床结局特征的ER+ (luminal A/luminal B1 - ER low)样和ER-样乳腺肿瘤的重新分类

Guisong Wang, Punit Shah, R. Searfoss, Leigh Fantacone-Campbell, J. Hooke, B. Deyarmin, Rebecca N. Zingmark, S. Somiari, Jianfang Liu, L. Kvecher, Bradley J. Mostoller, Lori A. Sturtz, Praven-Kumar Raj-Kumar, E. Granger, L. Vahdat, M. Cutler, C. Bountra, R. Sarangarajan, Hai Hu, M. Kiebish, A. Kovatich, N. Narain, C. Shriver
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Utilizing an integrated bioinformatics approach, we developed a proteomic marker signature to reclassify tumors into ER+(like) and ER-(like) tumors. CPTAC (Proteomic)/TCGA (RNAseq) datasets and larger METBRIC and GSE96058 cohorts were used to validate this marker signature. The selected biomarkers demonstrated significant differences impacting survival outcome. Methods: Clinical IHC subtyping of core biopsies was used to select a cohort of patients with ER+/HER2- and ER-/HER2- primary tumors from flash-frozen surgical samples. The positive/negative status of ER/PR/HER2 was defined using updated ASCO 2020 guidelines. Ki-67 status was determined using the 2011 St. Gallen9s International Expert Consensus recommendations. Proteomic analysis was performed using Thermo Q-Exactive+ LC MS/MS analysis. Differential analysis was applied to select the significantly altered proteins between ER+ and ER- cases, Univariate survival analysis was engaged to filter informative protein/genes using TCGA RNA-Seq data. Nearest centroid analysis was deployed to define the classifier to predict novel molecular subtypes. Results/Conclusions: We selected 34 proteins/genes from 164 significantly differentially expressed proteins for further analysis. The centroid model constructed with the 34 proteins defined 2 groups: ER+(like) and ER-(like). An additional 4 groups were defined across subtypes: luminal tumors classified both by IHC and marker signature (LL), luminal tumors classified by IHC but marker signature more like triple negative (LT), triple negative tumors classified by IHC but marker signature more like luminal (TL), and triple negative classified by both IHC and marker signature (TT). This marker signature segregated close to 5000 tumors across CPTAC, TCGA, METABRIC and GSE96058 cohorts. Survival analysis in these groups of patients revealed differences in radiation, hormone/radiation, hormone therapy, and hormone/radiation/chemotherapy treatments. In summary using proteomics data we identified a 34 gene/protein marker signature, validated in large external cohorts and exhibited impact on survival and response to therapy. Further, this signature was enriched in metabolism and microenvironmental associated factors that could represent novel targets or development combination strategies based on this signature. Citation Format: Guisong Wang, Punit Shah, Rick Searfoss, Leigh Fantacone-Campbell, Jeffrey A. Hooke, Brenda Deyarmin, Rebecca N. Zingmark, Stella Somiari, Jianfang Liu, Leonid Kvecher, Bradley Mostoller, Lori A. Sturtz, Praven-Kumar Raj-Kumar, Elder Granger, Linda Vahdat, Mary L. Cutler, Chas Bountra, Rangaprasad Sarangarajan, Hai Hu, Michael A. Kiebish, Albert J. Kovatich, Niven R. Narain, Craig D. Shriver. 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引用次数: 0

摘要

乳腺癌的分型可结合免疫组化(IHC)检测ER/PR/HER2/KI67进行亚型分型。高通量蛋白质组学分析允许在亚型中扩展生物标志物的发现。我们评估了109例ER+肿瘤(Luminal a和Luminal B1;与ER-/HER2-肿瘤相比,HER2+和ER低(1-10%)的病例被排除。利用综合生物信息学方法,我们开发了一种蛋白质组学标记标记,将肿瘤重新分类为ER+(样)和ER-(样)肿瘤。使用CPTAC (Proteomic)/TCGA (RNAseq)数据集和更大的METBRIC和GSE96058队列来验证该标记签名。所选择的生物标志物显示出影响生存结果的显着差异。方法:采用核心活检的临床免疫组化分型,从速冻手术样本中选择ER+/HER2-和ER-/HER2-原发肿瘤患者。使用更新的ASCO 2020指南定义ER/PR/HER2的阳性/阴性状态。Ki-67状态是根据2011年圣加仑国际专家共识建议确定的。蛋白质组学分析采用Thermo Q-Exactive+ LC MS/MS分析。采用差异分析选择ER+和ER-病例之间显著改变的蛋白,采用单因素生存分析使用TCGA RNA-Seq数据过滤信息蛋白/基因。最近质心分析被用来定义分类器来预测新的分子亚型。结果/结论:我们从164个显著差异表达蛋白中筛选出34个蛋白/基因进行进一步分析。34种蛋白构建质心模型,分为2组:ER+(like)和ER-(like)。另外,根据亚型划分了4组:经免疫组化和标记标记分类的管腔肿瘤(LL)、经免疫组化分类但标记标记更接近三阴性(LT)的管腔肿瘤、经免疫组化分类但标记标记更接近三阴性(TL)的三阴性肿瘤、经免疫组化和标记标记同时分类的三阴性肿瘤(TT)。该标记在CPTAC、TCGA、METABRIC和GSE96058队列中分离了近5000个肿瘤。这些患者的生存分析揭示了放疗、激素/放疗、激素治疗和激素/放疗/化疗治疗的差异。总之,利用蛋白质组学数据,我们确定了34个基因/蛋白质标记,在大型外部队列中得到验证,并显示出对生存和治疗反应的影响。此外,该特征富含代谢和微环境相关因子,可能代表基于该特征的新靶点或开发组合策略。引用格式:Guisong Wang, Punit Shah, Rick Searfoss, Leigh Fantacone-Campbell, Jeffrey A. Hooke, Brenda Deyarmin, Rebecca N. Zingmark, Stella Somiari,刘建芳,Leonid Kvecher, Bradley Mostoller, Lori A. Sturtz, prven - kumar Raj-Kumar, Elder Granger, Linda Vahdat, Mary L. Cutler, Chas Bountra, Rangaprasad Sarangarajan, Hai Hu, Michael A. Kiebish, Albert J. Kovatich, Niven R. Narain, Craig D. Shriver。基于蛋白质组学/基因和临床结局特征的ER+ (luminal A/luminal B1 - ER low)样和ER-样乳腺肿瘤的重新分类[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):1188。
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Abstract 1188: Reclassification of ER+ (luminal A/luminal B1 minus ER low)-like and ER- like breast tumors based on proteomic/gene and clinical outcome signatures
Introduction: Classification of breast cancer can incorporate immunohistochemical (IHC) detection of ER/PR/HER2/KI67 to stratify the subtypes. High throughput proteomics analysis allows for the expansion of biomarker discovery within the subtypes. We evaluated a cohort of 109 tumors characterized as ER+ (Luminal A and Luminal B1; HER2+ and ER low (1-10%) cases were excluded) compared to ER-/HER2- tumors. Utilizing an integrated bioinformatics approach, we developed a proteomic marker signature to reclassify tumors into ER+(like) and ER-(like) tumors. CPTAC (Proteomic)/TCGA (RNAseq) datasets and larger METBRIC and GSE96058 cohorts were used to validate this marker signature. The selected biomarkers demonstrated significant differences impacting survival outcome. Methods: Clinical IHC subtyping of core biopsies was used to select a cohort of patients with ER+/HER2- and ER-/HER2- primary tumors from flash-frozen surgical samples. The positive/negative status of ER/PR/HER2 was defined using updated ASCO 2020 guidelines. Ki-67 status was determined using the 2011 St. Gallen9s International Expert Consensus recommendations. Proteomic analysis was performed using Thermo Q-Exactive+ LC MS/MS analysis. Differential analysis was applied to select the significantly altered proteins between ER+ and ER- cases, Univariate survival analysis was engaged to filter informative protein/genes using TCGA RNA-Seq data. Nearest centroid analysis was deployed to define the classifier to predict novel molecular subtypes. Results/Conclusions: We selected 34 proteins/genes from 164 significantly differentially expressed proteins for further analysis. The centroid model constructed with the 34 proteins defined 2 groups: ER+(like) and ER-(like). An additional 4 groups were defined across subtypes: luminal tumors classified both by IHC and marker signature (LL), luminal tumors classified by IHC but marker signature more like triple negative (LT), triple negative tumors classified by IHC but marker signature more like luminal (TL), and triple negative classified by both IHC and marker signature (TT). This marker signature segregated close to 5000 tumors across CPTAC, TCGA, METABRIC and GSE96058 cohorts. Survival analysis in these groups of patients revealed differences in radiation, hormone/radiation, hormone therapy, and hormone/radiation/chemotherapy treatments. In summary using proteomics data we identified a 34 gene/protein marker signature, validated in large external cohorts and exhibited impact on survival and response to therapy. Further, this signature was enriched in metabolism and microenvironmental associated factors that could represent novel targets or development combination strategies based on this signature. Citation Format: Guisong Wang, Punit Shah, Rick Searfoss, Leigh Fantacone-Campbell, Jeffrey A. Hooke, Brenda Deyarmin, Rebecca N. Zingmark, Stella Somiari, Jianfang Liu, Leonid Kvecher, Bradley Mostoller, Lori A. Sturtz, Praven-Kumar Raj-Kumar, Elder Granger, Linda Vahdat, Mary L. Cutler, Chas Bountra, Rangaprasad Sarangarajan, Hai Hu, Michael A. Kiebish, Albert J. Kovatich, Niven R. Narain, Craig D. Shriver. Reclassification of ER+ (luminal A/luminal B1 minus ER low)-like and ER- like breast tumors based on proteomic/gene and clinical outcome signatures [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 1188.
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