The E2F4 prognostic signature is also predictive of the pathological response of breast cancer to chemotherapy

Transcription Factor E2F4 Promote Proliferation, Migration, and Invasion of Gastric Cancer Cells by transcriptionally activating DSCC1

Gastric cancer (GC) ranks as the fifth most common cancer and the fourth leading cause of cancer-related deaths globally. Despite advancements in molecular profiling, the mechanisms driving GC proliferation and metastasis remain unclear. This study identifies Early 2 Factor 4 (E2F4) as a key transcription factor that promotes GC cell proliferation, migration, and invasion by upregulating DNA Replication and Sister Chromatid Cohesion 1 (DSCC1) expression. Bioinformatics and transcription factor analyses revealed E2F4 as a significant regulator of DSCC1. Functional assays confirmed E2F4's role in enhancing GC cell malignancy in vitro and in vivo. Knockdown and overexpression experiments demonstrated that E2F4 positively regulates DSCC1 at the transcriptional level, with ChIP-qPCR and dual luciferase reporter assays validating the binding sites on the DSCC1 promoter. These findings highlight the E2F4-DSCC1 axis as a potential therapeutic target to mitigate GC progression.

The E2F4 prognostic signature predicts pathological response to neoadjuvant chemotherapy in breast cancer patients

Background

Neoadjuvant chemotherapy is a key component of breast cancer treatment regimens and pathologic complete response to this therapy varies among patients. This is presumably due to differences in the molecular mechanisms that underlie each tumor’s disease pathology. Developing genomic clinical assays that accurately categorize responders from non-responders can provide patients with the most effective therapy for their individual disease.

Methods

We applied our previously developed E2F4 genomic signature to predict neoadjuvant chemotherapy response in breast cancer. E2F4 individual regulatory activity scores were calculated for 1129 patient samples across 5 independent breast cancer neoadjuvant chemotherapy datasets. Accuracy of the E2F4 signature in predicting neoadjuvant chemotherapy response was compared to that of the Oncotype DX and MammaPrint predictive signatures.

Results

In all datasets, E2F4 activity level was an accurate predictor of neoadjuvant chemotherapy response, with high E2F4 scores predictive of achieving pathologic complete response and low scores predictive of residual disease. These results remained significant even after stratifying patients by estrogen receptor (ER) status, tumor stage, and breast cancer molecular subtypes. Compared to the Oncotype DX and MammaPrint signatures, our E2F4 signature achieved similar performance in predicting neoadjuvant chemotherapy response, though all signatures performed better in ER+ tumors compared to ER- ones. The accuracy of our signature was reproducible across datasets and was maintained when refined from a 199-gene signature down to a clinic-friendly 33-gene panel.

Conclusion

Overall, we show that our E2F4 signature is accurate in predicting patient response to neoadjuvant chemotherapy. As this signature is more refined and comparable in performance to other clinically available gene expression assays in the prediction of neoadjuvant chemotherapy response, it should be considered when evaluating potential treatment options.

Electronic supplementary material

The online version of this article (doi:10.1186/s12885-017-3297-2) contains supplementary material, which is available to authorized users.

Integrative Genomic Analyses Yield Cell-Cycle Regulatory Programs with Prognostic Value

Liposarcoma is the second most common form of sarcoma, which has been categorized into four molecular subtypes, which are associated with differential prognosis of patients. However, the transcriptional regulatory programs associated with distinct histologic and molecular subtypes of liposarcoma have not been investigated. This study uses integrative analyses to systematically define the transcriptional regulatory programs associated with liposarcoma. Likewise, computational methods are used to identify regulatory programs associated with different liposarcoma subtypes, as well as programs that are predictive of prognosis. Further analysis of curated gene sets was used to identify prognostic gene signatures. The integration of data from a variety of sources, including gene expression profiles, transcription factor-binding data from ChIP-Seq experiments, curated gene sets, and clinical information of patients, indicated discrete regulatory programs (e.g., controlled by E2F1 and E2F4), with significantly different regulatory activity in one or multiple subtypes of liposarcoma with respect to normal adipose tissue. These programs were also shown to be prognostic, wherein liposarcoma patients with higher E2F4 or E2F1 activity associated with unfavorable prognosis. A total of 259 gene sets were significantly associated with patient survival in liposarcoma, among which > 50% are involved in cell cycle and proliferation.

IMPLICATIONS

These integrative analyses provide a general framework that can be applied to investigate the mechanism and predict prognosis of different cancer types.