DNA methylation markers panel can improve prediction of response to neoadjuvant chemotherapy in luminal B breast cancer

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作者
Vladimir O. Sigin
Alexey I. Kalinkin
Ekaterina B. Kuznetsova
Olga A. Simonova
Galina G. Chesnokova
Nikolai V. Litviakov
Elena M. Slonimskaya
Matvey M. Tsyganov
Marina K. Ibragimova
Ilya V. Volodin
Ilya I. Vinogradov
Maksim I. Vinogradov
Igor Y. Vinogradov
Sergey I. Kutsev
Vladimir V. Strelnikov
Dmitry V. Zaletaev
Alexander S. Tanas
机构
[1] Research Centre for Medical Genetics,
[2] 115522,undefined
[3] Moskvorechie St.1,undefined
[4] I.M. Sechenov First Moscow State Medical University,undefined
[5] 119991,undefined
[6] Trubetskaya St.8,undefined
[7] Tomsk Cancer Research Institute,undefined
[8] 634009,undefined
[9] Kooperativniy Lane,undefined
[10] 5,undefined
[11] National Research Tomsk State University,undefined
[12] 634050,undefined
[13] Lenin Ave,undefined
[14] 36,undefined
[15] Pathology and Anatomy Department with Pathology Laboratory,undefined
[16] Ryazan Regional Clinical Oncology Dispensary,undefined
[17] Sportivnaya St.13,undefined
[18] Ryazan State Medical University,undefined
[19] Vysokovoltnaya St.9,undefined
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摘要
Despite the advantages of neoadjuvant chemotherapy (NACT), associated toxicity is a serious complication that renders monitoring of the patients’ response to NACT highly important. Thus, prediction of tumor response to treatment is imperative to avoid exposure of potential non-responders to deleterious complications. We have performed genome-wide analysis of DNA methylation by XmaI-RRBS and selected CpG dinucleotides differential methylation of which discriminates luminal B breast cancer samples with different sensitivity to NACT. With this data, we have developed multiplex methylation sensitive restriction enzyme PCR (MSRE-PCR) protocol for determining the methylation status of 10 genes (SLC9A3, C1QL2, DPYS, IRF4, ADCY8, KCNQ2, TERT, SYNDIG1, SKOR2 and GRIK1) that distinguish BC samples with different NACT response. Analysis of these 10 markers by MSRE-PCR in biopsy samples allowed us to reveal three top informative combinations of markers, (1) IRF4 and C1QL2; (2) IRF4, C1QL2, and ADCY8; (3) IRF4, C1QL2, and DPYS, with the areas under ROC curves (AUCs) of 0.75, 0.78 and 0.74, respectively. A classifier based on IRF4 and C1QL2 better meets the diagnostic panel simplicity requirements, as it consists of only two markers. Diagnostic accuracy of the panel of these two markers is 0.75, with the sensitivity of 75% and specificity of 75%.
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