イワモト　タカユキ   Takayuki Iwamoto   岩本　高行    所属   川崎医科大学  医学部 臨床医学　乳腺甲状腺外科学    職種   講師
論文種別	総説
言語種別	英語
査読の有無	査読あり
招待の有無	招待あり
表題	Biomarkers of neoadjuvant/adjuvant chemotherapy for breast cancer.
掲載誌名	正式名：Chinese clinical oncology 略　 称：Chin Clin Oncol ISSNコード：23043873/23043865
掲載区分	国外
巻・号・頁	9(3),pp.27
著者・共著者	Takayuki Iwamoto, Yukiko Kajiwara, Yidan Zhu, Shigemichi Iha
担当区分	最終著者,責任著者
発行年月	2020/06
概要	The improvement of tumor biomarkers prepared for clinical use is a long process. A good biomarker should predict not only prognosis but also the response to therapies. In this review, we describe the biomarkers of neoadjuvant/adjuvant chemotherapy for breast cancer, considering different breast cancer subtypes. In hormone receptor (HR)-positive/human epidermal growth factor 2 (HER2)-negative breast cancers, various genomic markers highly associated with proliferation have been tested. Among them, only two genomic signatures, the 21-gene recurrence score and 70-gene signature, have been reported in prospective randomized clinical trials and met the primary endpoint. However, these genomic markers did not suffice in HER2-positive and triple-negative (TN) breast cancers, which present only classical clinical and pathological information (tumor size, nodal or distant metastatic status) for decision making in the adjuvant setting in daily clinic. Recently, patients with residual invasive cancer after neoadjuvant chemotherapy are at a high-risk of recurrence for metastasis, which, in turn, make these patients best applicants for clinical trials. Two clinical trials have shown improved outcomes with post-operative capecitabine and ado-trastuzumab emtansine treatment in patients with either TN or HER2-positive breast cancer, respectively, who had residual disease after neoadjuvant chemotherapy. Furthermore, tumor-infiltrating lymphocytes (TILs) have been reported to have a predictive value for prognosis and response to chemotherapy from the retrospective analyses. So far, TILs have to not be used to either withhold or prescribe chemotherapy based on the absence of standardized evaluation guidelines and confirmed information. To overcome the low reproducibility of evaluations of TILs, gene signatures or digital image analysis and machine learning algorithms with artificial intelligence may be useful for standardization of assessment for TILs in the future.
DOI	10.21037/cco.2020.01.06
PMID	32192349