Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, 900 North Glebe Road, Arlington, VA, 22203, USA.
Center for Computational Biology, Flatiron Institute, Simons Foundation, 162 Fifth Avenue, New York, NY, 10010, USA.
Sci Rep. 2021 Jan 11;11(1):385. doi: 10.1038/s41598-020-79603-5.
Exploring complex modularization of intracellular signal transduction pathways is critical to understanding aberrant cellular responses during disease development and drug treatment. IMPALA (Inferred Modularization of PAthway LAndscapes) integrates information from high throughput gene expression experiments and genome-scale knowledge databases to identify aberrant pathway modules, thereby providing a powerful sampling strategy to reconstruct and explore pathway landscapes. Here IMPALA identifies pathway modules associated with breast cancer recurrence and Tamoxifen resistance. Focusing on estrogen-receptor (ER) signaling, IMPALA identifies alternative pathways from gene expression data of Tamoxifen treated ER positive breast cancer patient samples. These pathways were often interconnected through cytoplasmic genes such as IRS1/2, JAK1, YWHAZ, CSNK2A1, MAPK1 and HSP90AA1 and significantly enriched with ErbB, MAPK, and JAK-STAT signaling components. Characterization of the pathway landscape revealed key modules associated with ER signaling and with cell cycle and apoptosis signaling. We validated IMPALA-identified pathway modules using data from four different breast cancer cell lines including sensitive and resistant models to Tamoxifen. Results showed that a majority of genes in cell cycle/apoptosis modules that were up-regulated in breast cancer patients with short survivals (< 5 years) were also over-expressed in drug resistant cell lines, whereas the transcription factors JUN, FOS, and STAT3 were down-regulated in both patient and drug resistant cell lines. Hence, IMPALA identified pathways were associated with Tamoxifen resistance and an increased risk of breast cancer recurrence. The IMPALA package is available at https://dlrl.ece.vt.edu/software/ .
探索细胞内信号转导途径的复杂模块化对于理解疾病发展和药物治疗过程中细胞异常反应至关重要。IMPALA（Pathway LAndscapes 推断模块化）整合了高通量基因表达实验和基因组规模知识数据库的信息，以识别异常途径模块，从而提供了一种强大的采样策略来重建和探索途径景观。在这里，IMPALA 确定了与乳腺癌复发和他莫昔芬耐药相关的途径模块。关注雌激素受体（ER）信号，IMPALA 从他莫昔芬治疗的 ER 阳性乳腺癌患者样本的基因表达数据中识别替代途径。这些途径通常通过细胞质基因（如 IRS1/2、JAK1、YWHAZ、CSNK2A1、MAPK1 和 HSP90AA1）相互连接，并与 ErbB、MAPK 和 JAK-STAT 信号成分显著富集。途径景观的特征描述揭示了与 ER 信号以及细胞周期和细胞凋亡信号相关的关键模块。我们使用来自四个不同乳腺癌细胞系的数据（包括对他莫昔芬敏感和耐药的模型）验证了 IMPALA 鉴定的途径模块。结果表明，在生存时间<5 年的乳腺癌患者中上调的细胞周期/凋亡模块中的大多数基因也在耐药细胞系中过表达，而转录因子 JUN、FOS 和 STAT3 在患者和耐药细胞系中均下调。因此，IMPALA 鉴定的途径与他莫昔芬耐药和乳腺癌复发风险增加有关。IMPALA 软件包可在 https://dlrl.ece.vt.edu/software/ 获得。
