Resistance to trastuzumab and concomitantly distal metastasis are leading causes of mortality in HER2-positive breast cancers, the molecular basis of which remains largely unknown. Here, we generated trastuzumab-resistant breast cancer cells with increased tumorigenicity and invasiveness compared with parental cells, and observed robust epithelial-mesenchymal transition (EMT) and consistently elevated TGF-b signaling in these cells. which Breast cancers remain the most common malignancies in women with one million newly diagnosed cases and 400,000 deaths worldwide per year.1 The vast majority of these deaths are attributed to distal metastasis and resistance to available therapeutics.

SUMMARY Paralogous transcription factors (TFs) are oftentimes reported to have identical DNA-binding motifs, despite the fact that they perform distinct regulatory functions. Differential genomic targeting by paralogous TFs is generally assumed to be due to interactions with protein co-factors or the chromatin environment. Using a computational-experimental framework called iMADS (integrative modeling and analysis of differential specificity), we show that, contrary to previous assumptions, paralogous TFs bind differently to genomic target sites even in vitro. We used iMADS to quantify, model, and analyze specificity differences between 11 TFs from 4 protein families. We found that paralogous TFs have diverged mainly at mediumand low-affinity sites, which are poorly captured by current motif models. We identify sequence and shape features differentially preferred by paralogous TFs, and we show that the intrinsic differences in specificity among paralogous TFs contribute to their differential in vivo binding. Thus, our study represents a step forward in deciphering the molecular mechanisms of differential specificity in TF families.

Approximately 20% of HER2 positive breast cancer develops disease recurrence after adjuvant trastuzumab treatment. This study aimed to develop a molecular prognostic model that can reliably stratify patients by risk of developing disease recurrence. Using miRNA microarrays, nine miRNAs that differentially expressed between the recurrent and non-recurrent patients were identified. Then, we validated the expression of these miRNAs using qRT-PCR in training set (n = 101), and generated a 2-miRNA (miR-4734 and miR-150-5p) based prognostic signature. The prognostic accuracy of this classifier was further confirmed in an internal testing set (n = 57), and an external independent testing set (n = 53). Besides, by comparing the ROC curves, we found the incorporation of this miRNA based classifier into TNM stage could improve the prognostic performance of TNM system. The results indicated the 2-miRNA based signature was a reliable prognostic biomarker for patients with HER2 positive breast cancer.

Increasing cell mobility is the basis of tumor invasion and metastasis, and is therefore a therapeutic target for preventing the spread of many types of cancer. Septins are a family of cytoskeletal proteins with GTPase activity, and play a role in many important cellular functions, including cell migration. SEPT9 isoform 1 protein (SEPT9_i1) has been associated with breast tumor development and the enhancement of cell migration; however, the exact mechanism of how SEPT9_i1 might affect breast cancer progression remains to be elucidated. Here, we report that the expression of SEPT9_i1 positively correlated with paxillin, and both were significantly upregulated in invasive breast cancer tissues of patients with lymph node metastases. Lentivirus-mediated shRNA knockdown of SEPT9 in MCF-7 cells diminished tumor cell migration, focal adhesion (FA) maturation and the expression of β-actin, β-tubulin, Cdc42, RhoA, and Rac, whereas overexpression of SEPT9_i1 in SEPT9-knockdown MCF-7 cells promoted cell migration, FA maturation and relevant protein expression. Furthermore, overexpression of SEPT9_i1 in MCF-7 cells markedly increased FAK/Src/paxillin signaling, at least in part through RhoA/ROCK1 upstream activation. Transcriptome profiling suggested that SEPT9_i1 may directly affect “Focal adhesion” and “Regulation of actin cytoskeleton” signaling mechanisms. Finally, overexpression of SEPT9_i1 markedly enhanced lung metastases in vivo 6 weeks after tumor inoculation. These findings suggest that a mechanism of Septin-9-induced aberrant cancer cell migration is through cytoskeletal regulation and FA modulation, and encourages the use of SEPT9 as novel therapeutic target in the prevention of tumor metastasis.

Non-coding genetic variants/mutations can play functional roles in the cell by disrupting regulatory interactions between transcription factors (TFs) and their genomic target sites. For most human TFs, a myriad of DNA-binding models are available and could be used to predict the effects of DNA mutations on TF binding. However, information on the quality of these models is scarce, making it hard to evaluate the statistical significance of predicted binding changes. Here, we present QBiC-Pred, a web server for predicting quantitative TF binding changes due to nucleotide variants. QBiC-Pred uses regression models of TF binding specificity trained on high-throughput in vitro data. The training is done using ordinary least squares (OLS), and we leverage distributional results associated with OLS estimation to compute, for each predicted change in TF binding, a P-value reflecting our confidence in the predicted effect. We show that OLS models are accurate in predicting the effects of mutations on TF binding in vitro and in vivo, outperforming widely-used PWM models as well as recently developed deep learning models of specificity. QBiC-Pred takes as input mutation datasets in several formats, and it allows post-processing of the results through a user-friendly web interface. QBiC-Pred is freely available at http://qbic.genome.duke.edu.