Molecular and clinical features of the TP53 signature gene expression profile in early-stage breast cancer

// Shigeo Yamaguchi 1 , Shin Takahashi 2 , Kaoru Mogushi 3 , Yuki Izumi 1 , Yumi Nozaki 1 , Tadashi Nomizu 4 , Yoichiro Kakugawa 5 , Takanori Ishida 6 , Noriaki Ohuchi 6 , Chikashi Ishioka 2 and Shunsuke Kato 1, 3 1 Department of Clinical Oncology, Juntendo University Graduated School, Tokyo 113-8421, Japan 2 Department of Clinical Oncology, Institute of Development, Aging and Cancer, Tohoku University, Sendai 980-8575, Japan 3 Diagnostics and Therapeutics of Intractable Diseases, Intractable Disease Research Center, Juntendo University Graduated School, Tokyo 113-8421, Japan 4 Department of Surgery, Hoshi General Hospital, Fukushima 963-8501, Japan 5 Department of Breast Oncology, Miyagi Cancer Center Hospital, Natori 981-1293, Japan 6 Department of Breast and Endocrine Surgical Oncology, Tohoku University Graduate School of Medicine, Sendai 980-8574, Japan Correspondence to: Chikashi Ishioka, email: chikashi@tohoku.ac.jp Keywords: TP53; breast cancer; genomic instability; transcriptome; prognostic biomarker Received: October 27, 2017      Accepted: January 30, 2018      Epub: February 08, 2018      Published: March 06, 2018 ABSTRACT Purpose: TP53 signature has a robust predictive performance for prognosis in early-stage breast cancer, but the experiment that reported this relied on public microarray data and fresh-frozen samples. Before TP53 signature can be used in a clinical setting, a simple and low-cost diagnostic system using formalin-fixed paraffin-embedded (FFPE) samples is needed. New treatments based on the biological characteristics of TP53 signature are expected to follow. Experimental Design: TP53 signature was evaluated in 174 FFPE early breast cancer specimens using digital quantification via the nCounter technique (NanoString). Patients were classified as TP53 signature mutant type ( n = 64) or wild type ( n = 110). Predictive power of TP53 signature was compared with those of other gene expression signatures in 153 fresh-frozen samples of the same cohort by RNA-seq. The molecular features of TP53 signature were elucidated using TCGA omics data and RNA-seq data to explore new therapeutic strategies for patients with TP53 signature mutant type. Results: TP53 signature was a strong predictor of prognosis and was also more accurate than other gene expression signatures and independent of other clinicopathological factors. TCGA data analysis showed that risk score of TP53 signature was an index of chromosomal and genomic instability and that TP53 signature mutant type was associated with higher PD-L1 expression, variation in copy numbers, and numbers of somatic mutations. Conclusions: TP53 signature as diagnosed using the nCounter system is not only a robust predictor of prognosis but also a potential predictor of responsiveness to immune checkpoint inhibitors.