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Rie Shibuya
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Takashi Suzuki
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Yasuhiro Miki
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Kimako Yoshida
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Takuya Moriya
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Katsuhiko Ono
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Jun-ichi Akahira
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Takanori Ishida Department of Pathology, Department of Surgery, Department of Surgery, Novartis Institutes for BioMedical Research Basel, Tohoku University School of Medicine, 2-1 Seiryo-machi, Aoba-ku, Sendai 980-8575, Japan

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Hisashi Hirakawa Department of Pathology, Department of Surgery, Department of Surgery, Novartis Institutes for BioMedical Research Basel, Tohoku University School of Medicine, 2-1 Seiryo-machi, Aoba-ku, Sendai 980-8575, Japan

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Dean B Evans Department of Pathology, Department of Surgery, Department of Surgery, Novartis Institutes for BioMedical Research Basel, Tohoku University School of Medicine, 2-1 Seiryo-machi, Aoba-ku, Sendai 980-8575, Japan

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Hironobu Sasano
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It is well known that sex steroids play important roles in the development of invasive ductal carcinoma (IDC) of the human breast. However, biological significance of sex steroids remains largely unclear in ductal carcinoma in situ (DCIS), regarded as a precursor lesion of IDC, which is partly due to the fact that the intratumoral concentration of sex steroids has not been examined in DCIS. Therefore, in this study, we first examined the intratumoral concentrations of estradiol and 5α-dihydrotestosterone (DHT) using liquid chromatography/electrospray tandem mass spectrometry in DCIS. Intratumoral concentrations of both estradiol and DHT were threefold higher in DCIS than non-neoplastic breast tissues and estrogen-producing enzymes (aromatase, steroid sulfatase, and 17β-hydroxysteroid dehydrogenase type 1 (17βHSD1)), and androgen-producing enzymes (17βHSD5 and 5α-reductase type 1 (5αRed1)) were abundantly expressed in DCIS by real-time PCR and immunohistochemical analyses. The intratumoral concentration of DHT was significantly lower in IDC than DCIS, while the expression of aromatase mRNA in carcinoma cells and intratumoral stromal cells was significantly higher in IDC than those in DCIS. Immunohistochemistry for sex steroid-producing enzymes in DCIS demonstrated that 5αRed1 immunoreactivity was positively correlated with Ki-67 labeling index and histological grade and was also associated with an increased risk of recurrence in patients with DCIS examined. Results of our study suggest that intratumoral concentrations of estradiol and DHT are increased in DCIS, which is possibly due to intratumoral production of these steroids. Therefore, estradiol and DHT may play important roles in the development of DCIS of the human breast.

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Philippe L Bedard
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Sandeep K Singhal Division of Medical Oncology and Hematology, Breast Cancer Translational Research Laboratory JC Heuson, Frontier Science (Scotland) Ltd, School of Medicine, Machine Learning Group, Novartis Institutes for BioMedical Research, Breakthrough Breast Research Group, University of Edinburgh, Department of Biostatistics and Computational Biology, Department of Medicine, Princess Margaret Hospital, University of Toronto, Toronto, Ontario, Canada

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Michail Ignatiadis Division of Medical Oncology and Hematology, Breast Cancer Translational Research Laboratory JC Heuson, Frontier Science (Scotland) Ltd, School of Medicine, Machine Learning Group, Novartis Institutes for BioMedical Research, Breakthrough Breast Research Group, University of Edinburgh, Department of Biostatistics and Computational Biology, Department of Medicine, Princess Margaret Hospital, University of Toronto, Toronto, Ontario, Canada

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Ian Bradbury Division of Medical Oncology and Hematology, Breast Cancer Translational Research Laboratory JC Heuson, Frontier Science (Scotland) Ltd, School of Medicine, Machine Learning Group, Novartis Institutes for BioMedical Research, Breakthrough Breast Research Group, University of Edinburgh, Department of Biostatistics and Computational Biology, Department of Medicine, Princess Margaret Hospital, University of Toronto, Toronto, Ontario, Canada
Division of Medical Oncology and Hematology, Breast Cancer Translational Research Laboratory JC Heuson, Frontier Science (Scotland) Ltd, School of Medicine, Machine Learning Group, Novartis Institutes for BioMedical Research, Breakthrough Breast Research Group, University of Edinburgh, Department of Biostatistics and Computational Biology, Department of Medicine, Princess Margaret Hospital, University of Toronto, Toronto, Ontario, Canada

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Benjamin Haibe-Kains Division of Medical Oncology and Hematology, Breast Cancer Translational Research Laboratory JC Heuson, Frontier Science (Scotland) Ltd, School of Medicine, Machine Learning Group, Novartis Institutes for BioMedical Research, Breakthrough Breast Research Group, University of Edinburgh, Department of Biostatistics and Computational Biology, Department of Medicine, Princess Margaret Hospital, University of Toronto, Toronto, Ontario, Canada
Division of Medical Oncology and Hematology, Breast Cancer Translational Research Laboratory JC Heuson, Frontier Science (Scotland) Ltd, School of Medicine, Machine Learning Group, Novartis Institutes for BioMedical Research, Breakthrough Breast Research Group, University of Edinburgh, Department of Biostatistics and Computational Biology, Department of Medicine, Princess Margaret Hospital, University of Toronto, Toronto, Ontario, Canada
Division of Medical Oncology and Hematology, Breast Cancer Translational Research Laboratory JC Heuson, Frontier Science (Scotland) Ltd, School of Medicine, Machine Learning Group, Novartis Institutes for BioMedical Research, Breakthrough Breast Research Group, University of Edinburgh, Department of Biostatistics and Computational Biology, Department of Medicine, Princess Margaret Hospital, University of Toronto, Toronto, Ontario, Canada

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Christine Desmedt Division of Medical Oncology and Hematology, Breast Cancer Translational Research Laboratory JC Heuson, Frontier Science (Scotland) Ltd, School of Medicine, Machine Learning Group, Novartis Institutes for BioMedical Research, Breakthrough Breast Research Group, University of Edinburgh, Department of Biostatistics and Computational Biology, Department of Medicine, Princess Margaret Hospital, University of Toronto, Toronto, Ontario, Canada

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Sherene Loi Division of Medical Oncology and Hematology, Breast Cancer Translational Research Laboratory JC Heuson, Frontier Science (Scotland) Ltd, School of Medicine, Machine Learning Group, Novartis Institutes for BioMedical Research, Breakthrough Breast Research Group, University of Edinburgh, Department of Biostatistics and Computational Biology, Department of Medicine, Princess Margaret Hospital, University of Toronto, Toronto, Ontario, Canada

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Dean B Evans Division of Medical Oncology and Hematology, Breast Cancer Translational Research Laboratory JC Heuson, Frontier Science (Scotland) Ltd, School of Medicine, Machine Learning Group, Novartis Institutes for BioMedical Research, Breakthrough Breast Research Group, University of Edinburgh, Department of Biostatistics and Computational Biology, Department of Medicine, Princess Margaret Hospital, University of Toronto, Toronto, Ontario, Canada

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Stefan Michiels Division of Medical Oncology and Hematology, Breast Cancer Translational Research Laboratory JC Heuson, Frontier Science (Scotland) Ltd, School of Medicine, Machine Learning Group, Novartis Institutes for BioMedical Research, Breakthrough Breast Research Group, University of Edinburgh, Department of Biostatistics and Computational Biology, Department of Medicine, Princess Margaret Hospital, University of Toronto, Toronto, Ontario, Canada

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J Michael Dixon Division of Medical Oncology and Hematology, Breast Cancer Translational Research Laboratory JC Heuson, Frontier Science (Scotland) Ltd, School of Medicine, Machine Learning Group, Novartis Institutes for BioMedical Research, Breakthrough Breast Research Group, University of Edinburgh, Department of Biostatistics and Computational Biology, Department of Medicine, Princess Margaret Hospital, University of Toronto, Toronto, Ontario, Canada

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William R Miller Division of Medical Oncology and Hematology, Breast Cancer Translational Research Laboratory JC Heuson, Frontier Science (Scotland) Ltd, School of Medicine, Machine Learning Group, Novartis Institutes for BioMedical Research, Breakthrough Breast Research Group, University of Edinburgh, Department of Biostatistics and Computational Biology, Department of Medicine, Princess Margaret Hospital, University of Toronto, Toronto, Ontario, Canada

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Martine J Piccart Division of Medical Oncology and Hematology, Breast Cancer Translational Research Laboratory JC Heuson, Frontier Science (Scotland) Ltd, School of Medicine, Machine Learning Group, Novartis Institutes for BioMedical Research, Breakthrough Breast Research Group, University of Edinburgh, Department of Biostatistics and Computational Biology, Department of Medicine, Princess Margaret Hospital, University of Toronto, Toronto, Ontario, Canada

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Christos Sotiriou Division of Medical Oncology and Hematology, Breast Cancer Translational Research Laboratory JC Heuson, Frontier Science (Scotland) Ltd, School of Medicine, Machine Learning Group, Novartis Institutes for BioMedical Research, Breakthrough Breast Research Group, University of Edinburgh, Department of Biostatistics and Computational Biology, Department of Medicine, Princess Margaret Hospital, University of Toronto, Toronto, Ontario, Canada

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The gene expression grade index (GGI) is a 97-gene algorithm that measures proliferation and divides intermediate histological grade tumors into two distinct groups. We investigated the association between early changes in GGI and clinical response to neoadjuvant letrozole and compared this to Ki67 values. The paired gene expression data at the beginning and after 10–14 days of neoadjuvant letrozole treatment were available for 52 post-menopausal patients with estrogen receptor (ER)-positive breast cancer. Baseline values and changes in GGI, Ki67, and RNA expression modules representing oncogenic signaling pathways were compared to sonographic tumor volume changes after 3 months of treatment in the subsets of patients defined by high and low baseline GGI. The clinical response was observed in 80% genomic low-grade (24/30) and 59% genomic high-grade (13/22) tumors (P=0.10). Low residual proliferation after 10–14 days of neoadjuvant letrozole therapy, measured by either GGI or Ki67, was associated with sonographic response in genomic high-grade (GGI, P=0.003; Ki67, P=0.017) but not genomic low-grade (GGI, P=0.25; Ki67, P=1.0) tumors. The analysis of expression modules suggested that sonographic response to letrozole in genomic high-grade tumors was associated with an early reduction in IGF1 signaling (unadjusted P=0.018). The major conclusion of this study is that the early assessment of proliferation after short-term endocrine therapy may be useful to evaluate endocrine responsiveness, particularly in genomic high-grade ER-positive breast cancer.

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