start-ver=1.4 cd-journal=joma no-vol=9 cd-vols= no-issue=3 article-no= start-page=27 end-page= dt-received= dt-revised= dt-accepted= dt-pub-year=2020 dt-pub=202007 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=Biomarkers of neoadjuvant/adjuvant chemotherapy for breast cancer en-subtitle= kn-subtitle= en-abstract= kn-abstract=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. en-copyright= kn-copyright= en-aut-name=IwamotoTakayuki en-aut-sei=Iwamoto en-aut-mei=Takayuki kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= en-aut-name=KajiwaraYukiko en-aut-sei=Kajiwara en-aut-mei=Yukiko kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=2 ORCID= en-aut-name=ZhuYidan en-aut-sei=Zhu en-aut-mei=Yidan kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=3 ORCID= en-aut-name=IhaShigemichi en-aut-sei=Iha en-aut-mei=Shigemichi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=4 ORCID= affil-num=1 en-affil=Department of Breast and Endocrine Surgery, Okayama University Hospital kn-affil= affil-num=2 en-affil=Department of Breast and Endocrine Surgery, Okayama University Hospital kn-affil= affil-num=3 en-affil=Department of Breast and Endocrine Surgery, Okayama University Hospital kn-affil= affil-num=4 en-affil=Department of Breast Oncology, Miyake Ofuku Clinic kn-affil= en-keyword=Biomarker kn-keyword=Biomarker en-keyword=chemotherapy kn-keyword=chemotherapy en-keyword=breast cancer kn-keyword=breast cancer en-keyword=gene expression kn-keyword=gene expression END