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

• Published in: BMC cancer Vol. 17; no. 1; p. 306
• Main Authors: Mark, Kenneth M K, Varn, Frederick S, Ung, Matthew H, Qian, Feng, Cheng, Chao
• Format: Journal Article
• Language: English
• Published: England BioMed Central 02-05-2017; BMC
• Subjects: Biopsy; Breast cancer; Breast Neoplasms - drug therapy; Breast Neoplasms - metabolism; Breast Neoplasms - mortality; Breast Neoplasms - pathology; Cancer therapies; Cell cycle; Chemotherapy; ChIP-seq; Chromatin Immunoprecipitation; Databases, Factual; Datasets; Disease; E2F4; E2F4 Transcription Factor - analysis; E2F4 Transcription Factor - chemistry; E2F4 Transcription Factor - genetics; E2F4 Transcription Factor - metabolism; Estrogen receptors; Female; Gene expression; Genomes; Genomics; Humans; Medical prognosis; Molecular modelling; Neoadjuvant chemotherapy; Neoadjuvant Therapy; Pathologic complete response; Patients; Prognosis; ROC Curve; Therapeutic applications; Transcription factor; Transcription factors; Tumors; Neoadjuvant chemotherapy; ChIP-seq; Pathologic complete response; Breast cancer; E2F4; Transcription factor
• ISSN: 1471-2407; 1471-2407
• DOI: 10.1186/s12885-017-3297-2

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.
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.
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.
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.