Spatial Profiling of the Prostate Cancer Tumor Microenvironment Reveals Multiple Differences in Gene Expression and Correlation with Recurrence Risk.
Spatial Profiling of the Prostate Cancer Tumor Microenvironment Reveals Multiple Differences in Gene Expression and Correlation with Recurrence Risk.
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在前列腺癌（PCa）中，肿瘤微环境在疾病的发展和进展中起着至关重要的作用。我们使用数字空间分析多重技术来评估传统挑战性样品中58种蛋白质和1825种RNA转录物的表达（即，存档的FFPE样品），同时保持表达的空间背景。首先，我们确定了多种细胞类型（基质细胞、上皮细胞和免疫细胞）之间蛋白质和RNA表达的差异。其次，我们使用了1547个核心（97例患者）的PCa组织微阵列，以进一步验证在更大的前列腺癌患者队列中选定基因的蛋白质表达。我们还计算了生存模型，测试肿瘤和肿瘤相邻间质样本中OX 40 L、CTLA 4和CD 11 c蛋白表达与生化复发时间之间的关系。肿瘤微环境在前列腺癌的发生和发展中起着至关重要的作用。此外，识别不同区域之间的蛋白质和基因表达差异对于治疗开发是有价值的。我们将数字空间轮廓多重分析应用于福尔马林固定的石蜡包埋的乳腺癌切除术组织块，以研究肿瘤、肿瘤相邻间质（TAS）、CD 45+肿瘤和CD 45 + TAS组织之间的蛋白质和转录组差异。测量免疫学/肿瘤学蛋白质组（n = 58）的差异表达。与肿瘤上皮细胞相比，TAS中OX 40 L和CTLA 4以较高水平表达，而22种其他蛋白质（包括CD 11 c）以较低水平表达（FDR < 0.2，p值< 0.05）。对97例患者（1547个核心）的组织微阵列分析发现，CD 11 c高表达与肿瘤和TAS的复发时间增加呈正相关，与CTLA 4和OX 40 L呈负相关，其中肿瘤中较高的表达与较短的复发时间相关，但TAS的复发时间较高。使用Cancer Transcriptome Atlas panel（n = 1825个基因）进行的空间转录组学分析确定了TAS与肿瘤中162个基因下调和69个基因上调，CD 45 + TAS与CD 45+肿瘤中26个基因下调和6个基因上调。我们使用CIBERSORTx来估计使用CD 45+基因表达的相对免疫细胞分数，并发现TAS中记忆B、幼稚B和T细胞的平均分数较高。总之，蛋白质表达差异、免疫细胞分数和蛋白质表达与复发时间的相关性的组合表明，密切检查肿瘤微环境提供了可以改善诊断和治疗技术的有价值的数据。
In prostate cancer (PCa), the tumor microenvironment plays a crucial role in both the development and progression of the disease. We used Digital Spatial Profiling multiplex technology to assess the expression of 58 protein and 1825 RNA transcripts in traditionally challenging samples (i.e., archived FFPE samples) while maintaining the spatial context of expression. First, we identified differences in protein and RNA expression among multiple cell types (stromal, epithelial, and immune cells). Second, we used a PCa tissue microarray of 1547 cores (97 patients) to further validate the protein expression of selected genes in a larger cohort of prostate cancer patients. We also computed survival models testing the relationship between OX40L, CTLA4, and CD11c protein expression in both tumor and tumor-adjacent stroma samples with time to biochemical relapse. The tumor microenvironment plays a crucial role in both the development and progression of prostate cancer. Furthermore, identifying protein and gene expression differences between different regions is valuable for treatment development. We applied Digital Spatial Profiling multiplex analysis to formalin-fixed paraffin embedded prostatectomy tissue blocks to investigate protein and transcriptome differences between tumor, tumor-adjacent stroma (TAS), CD45+ tumor, and CD45+ TAS tissue. Differential expression of an immunology/oncology protein panel (n = 58) was measured. OX40L and CTLA4 were expressed at higher levels while 22 other proteins, including CD11c, were expressed at lower levels (FDR < 0.2 and p-value < 0.05) in TAS as compared to tumor epithelia. A tissue microarray analysis of 97 patients with 1547 cores found positive correlations between high expression of CD11c and increased time to recurrence in tumor and TAS, and inverse relationships for CTLA4 and OX40L, where higher expression in tumor correlated with lower time to recurrence, but higher time to recurrence in TAS. Spatial transcriptomic analysis using a Cancer Transcriptome Atlas panel (n = 1825 genes) identified 162 genes downregulated and 69 upregulated in TAS versus tumor, 26 downregulated and 6 upregulated in CD45+ TAS versus CD45+ tumor. We utilized CIBERSORTx to estimate the relative immune cell fractions using CD45+ gene expression and found higher average fractions for memory B, naïve B, and T cells in TAS. In summary, the combination of protein expression differences, immune cell fractions, and correlations of protein expression with time to recurrence suggest that closely examining the tumor microenvironment provides valuable data that can improve prognostication and treatment techniques.
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影响因子：
4.6
作者：
Bankhead P;Loughrey MB;Fernández JA;Dombrowski Y;McArt DG;Dunne PD;McQuaid S;Gray RT;Murray LJ;Coleman HG;James JA;Salto-Tellez M;Hamilton PW
通讯作者：
Hamilton PW
烟酰胺N-甲基转移酶是预测泌尿外科癌症的肿瘤学结局的潜在生物标志物：更新。
影响因子：
5.5
作者：
Campagna R;Pozzi V;Spinelli G;Sartini D;Milanese G;Galosi AB;Emanuelli M
通讯作者：
Emanuelli M
影响因子：
64.8
作者：
Helmink BA;Reddy SM;Gao J;Zhang S;Basar R;Thakur R;Yizhak K;Sade-Feldman M;Blando J;Han G;Gopalakrishnan V;Xi Y;Zhao H;Amaria RN;Tawbi HA;Cogdill AP;Liu W;LeBleu VS;Kugeratski FG;Patel S;Davies MA;Hwu P;Lee JE;Gershenwald JE;Lucci A;Arora R;Woodman S;Keung EZ;Gaudreau PO;Reuben A;Spencer CN;Burton EM;Haydu LE;Lazar AJ;Zapassodi R;Hudgens CW;Ledesma DA;Ong S;Bailey M;Warren S;Rao D;Krijgsman O;Rozeman EA;Peeper D;Blank CU;Schumacher TN;Butterfield LH;Zelazowska MA;McBride KM;Kalluri R;Allison J;Petitprez F;Fridman WH;Sautès-Fridman C;Hacohen N;Rezvani K;Sharma P;Tetzlaff MT;Wang L;Wargo JA
通讯作者：
Wargo JA
影响因子：
6.4
作者：
Hatzifoti, Caterina;Heath, Andrew W.
通讯作者：
Heath, Andrew W.
影响因子：
64.8
作者：
Cabrita, Rita;Lauss, Martin;Jonsson, Goran
通讯作者：
Jonsson, Goran