Differential and longitudinal immune gene patterns associated with reprogrammed microenvironment and viral mimicry in response to neoadjuvant radiotherapy in rectal cancer.
Differential and longitudinal immune gene patterns associated with reprogrammed microenvironment and viral mimicry in response to neoadjuvant radiotherapy in rectal cancer.
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直肠癌新辅助放疗后与重编程微环境和病毒模拟相关的差异和纵向免疫基因模式。
DOI：
10.1136/jitc-2020-001717
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发表时间：
2021-03
影响因子：
10.9
通讯作者：
Sadanandam A
中科院分区：
文献类型：
--
作者：
Wilkins A;Fontana E;Nyamundanda G;Ragulan C;Patil Y;Mansfield D;Kingston J;Errington-Mais F;Bottomley D;von Loga K;Bye H;Carter P;Tinkler-Hundal E;Noshirwani A;Downs J;Dillon M;Demaria S;Sebag-Montefiore D;Harrington K;West N;Melcher A;Sadanandam A
直肠癌对新辅助放疗/放化疗（RT/CRT）的反应差异很大，肿瘤免疫微环境对这种反应的影响尚不清楚。目前的临床肿瘤消退分级系统试图测量放射治疗反应，但受观察者间差异的影响。无偏和独特的组织病理学定量方法（肿瘤细胞密度变化（ΔTCD））可能会改善RT/CRT反应的分类。此外，免疫基因表达谱（GEP）可以识别与不同放射治疗反应相关的基因表达水平的差异：（1）在基线时不良反应者和良好反应者之间，以及（2）从放射治疗前到放射治疗后样品的纵向差异。总的来说，这可能为直肠癌的新型治疗RT/CRT组合策略提供信息。我们对53例术前放疗前活检的患者进行了GEP。TCD用于评估直肠肿瘤对新辅助RT/CRT的反应，并对ΔTCD进行k均值聚类以将患者分类为不同的反应类别。使用微阵列的统计分析、途径富集分析和使用单样品基因集富集分析的免疫细胞类型分析进行差异基因表达分析。进行免疫组织化学以验证特异性结果。使用来自公开数据集的220个预处理样本在途径和生存分析的元水平上对结果进行验证。ΔTCD评分范围为12.4%至−47.7%，并将患者分为3个反应类别。在基线时，40个基因在不良反应者（n=12）与良好反应者（n=21）中显著上调，包括骨髓和基质细胞基因。在不良反应者基线时显示显著富集的几种途径中，在外部队列中验证了上皮向间质转化、凝血、补体激活和顶端连接途径。与不良反应者不同，良好反应者显示198个免疫基因的纵向（放疗前与放疗后样品）上调，反映了T细胞炎症GEP，I型干扰素和巨噬细胞群体的增加。纵向路径分析表明，病毒样病原体反应发生在治疗后切除的样本相比，治疗前活检的良好反应。这项研究表明，在基线时反应差的患者中可能存在可药物治疗的免疫靶点，并表明具有良好RT/CRT反应的肿瘤在放疗治疗时从免疫“冷”向免疫“热”表型重编程。
Rectal cancers show a highly varied response to neoadjuvant radiotherapy/chemoradiation (RT/CRT) and the impact of the tumor immune microenvironment on this response is poorly understood. Current clinical tumor regression grading systems attempt to measure radiotherapy response but are subject to interobserver variation. An unbiased and unique histopathological quantification method (change in tumor cell density (ΔTCD)) may improve classification of RT/CRT response. Furthermore, immune gene expression profiling (GEP) may identify differences in expression levels of genes relevant to different radiotherapy responses: (1) at baseline between poor and good responders, and (2) longitudinally from preradiotherapy to postradiotherapy samples. Overall, this may inform novel therapeutic RT/CRT combination strategies in rectal cancer. We generated GEPs for 53 patients from biopsies taken prior to preoperative radiotherapy. TCD was used to assess rectal tumor response to neoadjuvant RT/CRT and ΔTCD was subjected to k-means clustering to classify patients into different response categories. Differential gene expression analysis was performed using statistical analysis of microarrays, pathway enrichment analysis and immune cell type analysis using single sample gene set enrichment analysis. Immunohistochemistry was performed to validate specific results. The results were validated using 220 pretreatment samples from publicly available datasets at metalevel of pathway and survival analyses. ΔTCD scores ranged from 12.4% to −47.7% and stratified patients into three response categories. At baseline, 40 genes were significantly upregulated in poor (n=12) versus good responders (n=21), including myeloid and stromal cell genes. Of several pathways showing significant enrichment at baseline in poor responders, epithelial to mesenchymal transition, coagulation, complement activation and apical junction pathways were validated in external cohorts. Unlike poor responders, good responders showed longitudinal (preradiotherapy vs postradiotherapy samples) upregulation of 198 immune genes, reflecting an increased T-cell-inflamed GEP, type-I interferon and macrophage populations. Longitudinal pathway analysis suggested viral-like pathogen responses occurred in post-treatment resected samples compared with pretreatment biopsies in good responders. This study suggests potentially druggable immune targets in poor responders at baseline and indicates that tumors with a good RT/CRT response reprogrammed from immune “cold” towards an immunologically “hot” phenotype on treatment with radiotherapy.
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DOI：
10.6004/jnccn.2018.0061
发表时间：
2018-07
期刊：
Journal of the National Comprehensive Cancer Network : JNCCN
影响因子：
--
作者：
Benson AB;Venook AP;Al-Hawary MM;Cederquist L;Chen YJ;Ciombor KK;Cohen S;Cooper HS;Deming D;Engstrom PF;Grem JL;Grothey A;Hochster HS;Hoffe S;Hunt S;Kamel A;Kirilcuk N;Krishnamurthi S;Messersmith WA;Meyerhardt J;Mulcahy MF;Murphy JD;Nurkin S;Saltz L;Sharma S;Shibata D;Skibber JM;Sofocleous CT;Stoffel EM;Stotsky-Himelfarb E;Willett CG;Wuthrick E;Gregory KM;Gurski L;Freedman-Cass DA
通讯作者：
Freedman-Cass DA
影响因子：
50.5
作者：
Goncalves-Ribeiro, S.;Sanz-Pamplona, R.;Mollevi, D. G.
通讯作者：
Mollevi, D. G.
影响因子：
4.6
作者：
Fontana E;Nyamundanda G;Cunningham D;Tu D;Cheang MCU;Jonker DJ;Siu LL;Sclafani F;Eason K;Ragulan C;Bali MA;Hulkki-Wilson S;Loree JM;Waring PM;Giordano M;Lawrence P;Rodrigues DN;Begum R;Shapiro JD;Price TJ;Cremolini C;Starling N;Pietrantonio F;Trusolino L;O'Callaghan CJ;Sadanandam A
通讯作者：
Sadanandam A
影响因子：
11.5
作者：
Kamran, Sophia C.;Lennerz, Jochen K.;Van Allen, Eliezer M.
通讯作者：
Van Allen, Eliezer M.
DOI：
10.1093/annonc/mdy161
发表时间：
2018-10-01
期刊：
Annals of oncology : official journal of the European Society for Medical Oncology
影响因子：
--
作者：
Glynne-Jones, R;Wyrwicz, L;Arnold, D
通讯作者：
Arnold, D