Proteomic Approach Reveals FKBP4 and S100A9 as Potential Prediction Markers of Therapeutic Response to Neoadjuvant Chemotherapy in Patients with Breast Cancer

Although doxorubicin (Doxo) and docetaxel (Docet) in combination are widely used in treatment regimens for a broad spectrum of breast cancer patients, a major obstacle has emerged in that some patients are intrinsically resistant to these chemotherapeutics. Our study aimed to discover potential prediction markers of drug resistance in needle-biopsied tissues of breast cancer patients prior to neoadjuvant chemotherapy. Tissues collected before chemotherapy were analyzed by mass spectrometry. A total of 2,331 proteins were identified and comparatively quantified between drug sensitive (DS) and drug resistant (DR) patient groups by spectral count. Of them, 298 proteins were differentially expressed by more than 1.5-fold. Some of the differentially expressed proteins (DEPs) were further confirmed by Western blotting. Bioinformatic analysis revealed that the DEPs were largely associated with drug metabolism, acute phase response signaling, and fatty acid elongation in mitochondria. Clinical validation of two selected proteins by immunohistochemistry found that FKBP4 and S100A9 might be putative prediction markers in discriminating the DR group from the DS group of breast cancer patients. The results demonstrate that a quantitative proteomics/bioinformatics approach is useful for discovering prediction markers of drug resistance, and possibly for the development of a new therapeutic strategy

Proteomic Approach Reveals FKBP4 and S100A9 as Potential Prediction Markers of Therapeutic Response to Neoadjuvant Chemotherapy in Patients with Breast Cancer

Although doxorubicin (Doxo) and docetaxel (Docet) in combination are widely used in treatment regimens for a broad spectrum of breast cancer patients, a major obstacle has emerged in that some patients are intrinsically resistant to these chemotherapeutics. Our study aimed to discover potential prediction markers of drug resistance in needle-biopsied tissues of breast cancer patients prior to neoadjuvant chemotherapy. Tissues collected before chemotherapy were analyzed by mass spectrometry. A total of 2,331 proteins were identified and comparatively quantified between drug sensitive (DS) and drug resistant (DR) patient groups by spectral count. Of them, 298 proteins were differentially expressed by more than 1.5-fold. Some of the differentially expressed proteins (DEPs) were further confirmed by Western blotting. Bioinformatic analysis revealed that the DEPs were largely associated with drug metabolism, acute phase response signaling, and fatty acid elongation in mitochondria. Clinical validation of two selected proteins by immunohistochemistry found that FKBP4 and S100A9 might be putative prediction markers in discriminating the DR group from the DS group of breast cancer patients. The results demonstrate that a quantitative proteomics/bioinformatics approach is useful for discovering prediction markers of drug resistance, and possibly for the development of a new therapeutic strategy

Proteomic Approach Reveals FKBP4 and S100A9 as Potential Prediction Markers of Therapeutic Response to Neoadjuvant Chemotherapy in Patients with Breast Cancer

Although doxorubicin (Doxo) and docetaxel (Docet) in combination are widely used in treatment regimens for a broad spectrum of breast cancer patients, a major obstacle has emerged in that some patients are intrinsically resistant to these chemotherapeutics. Our study aimed to discover potential prediction markers of drug resistance in needle-biopsied tissues of breast cancer patients prior to neoadjuvant chemotherapy. Tissues collected before chemotherapy were analyzed by mass spectrometry. A total of 2,331 proteins were identified and comparatively quantified between drug sensitive (DS) and drug resistant (DR) patient groups by spectral count. Of them, 298 proteins were differentially expressed by more than 1.5-fold. Some of the differentially expressed proteins (DEPs) were further confirmed by Western blotting. Bioinformatic analysis revealed that the DEPs were largely associated with drug metabolism, acute phase response signaling, and fatty acid elongation in mitochondria. Clinical validation of two selected proteins by immunohistochemistry found that FKBP4 and S100A9 might be putative prediction markers in discriminating the DR group from the DS group of breast cancer patients. The results demonstrate that a quantitative proteomics/bioinformatics approach is useful for discovering prediction markers of drug resistance, and possibly for the development of a new therapeutic strategy

Additional file 1: of Dietary pattern and health-related quality of life among breast cancer survivors

Tables S1-S12. Subgroup analyses. (DOCX 106 kb

Additional file 1 of Oncologic outcomes after immediate breast reconstruction following mastectomy: comparison of implant and flap using propensity score matching

Additional file 1. Fig. S1. CONSORT flow diagra

Korean Risk Assessment Model for Breast Cancer Risk Prediction

<div><p>Purpose</p><p>We evaluated the performance of the Gail model for a Korean population and developed a Korean breast cancer risk assessment tool (KoBCRAT) based upon equations developed for the Gail model for predicting breast cancer risk.</p><p>Methods</p><p>Using 3,789 sets of cases and controls, risk factors for breast cancer among Koreans were identified. Individual probabilities were projected using Gail's equations and Korean hazard data. We compared the 5-year and lifetime risk produced using the modified Gail model which applied Korean incidence and mortality data and the parameter estimators from the original Gail model with those produced using the KoBCRAT. We validated the KoBCRAT based on the expected/observed breast cancer incidence and area under the curve (AUC) using two Korean cohorts: the Korean Multicenter Cancer Cohort (KMCC) and National Cancer Center (NCC) cohort.</p><p>Results</p><p>The major risk factors under the age of 50 were family history, age at menarche, age at first full-term pregnancy, menopausal status, breastfeeding duration, oral contraceptive usage, and exercise, while those at and over the age of 50 were family history, age at menarche, age at menopause, pregnancy experience, body mass index, oral contraceptive usage, and exercise. The modified Gail model produced lower 5-year risk for the cases than for the controls (<i>p</i> = 0.017), while the KoBCRAT produced higher 5-year and lifetime risk for the cases than for the controls (<i>p</i><0.001 and <0.001, respectively). The observed incidence of breast cancer in the two cohorts was similar to the expected incidence from the KoBCRAT (KMCC, <i>p</i> = 0.880; NCC, <i>p</i> = 0.878). The AUC using the KoBCRAT was 0.61 for the KMCC and 0.89 for the NCC cohort.</p><p>Conclusions</p><p>Our findings suggest that the KoBCRAT is a better tool for predicting the risk of breast cancer in Korean women, especially urban women.</p></div

Additional file 3 of Oncologic outcomes after immediate breast reconstruction following mastectomy: comparison of implant and flap using propensity score matching

Additional file3: Table S2. Hazard ratio and p-value of disease-free interval using a Cox proportional hazard model in multivariate analysi

Summary of the patient characteristics.

Summary of the patient characteristics.</p

The Associations between Immunity-Related Genes and Breast Cancer Prognosis in Korean Women

<div><p>We investigated the role of common genetic variation in immune-related genes on breast cancer disease-free survival (DFS) in Korean women. 107 breast cancer patients of the Seoul Breast Cancer Study (SEBCS) were selected for this study. A total of 2,432 tag single nucleotide polymorphisms (SNPs) in 283 immune-related genes were genotyped with the GoldenGate Oligonucleotide pool assay (OPA). A multivariate Cox-proportional hazard model and polygenic risk score model were used to estimate the effects of SNPs on breast cancer prognosis. Harrell’s C index was calculated to estimate the predictive accuracy of polygenic risk score model. Subsequently, an extended gene set enrichment analysis (GSEA-SNP) was conducted to approximate the biological pathway. In addition, to confirm our results with current evidence, previous studies were systematically reviewed. Sixty-two SNPs were statistically significant at <i>p</i>-value less than 0.05. The most significant SNPs were rs1952438 in <i>SOCS4</i> gene (hazard ratio (HR) = 11.99, 95% CI = 3.62–39.72, <i>P</i> = 4.84E-05), rs2289278 in <i>TSLP</i> gene (HR = 4.25, 95% CI = 2.10–8.62, <i>P</i> = 5.99E-05) and rs2074724 in <i>HGF</i> gene (HR = 4.63, 95% CI = 2.18–9.87, <i>P</i> = 7.04E-05). In the polygenic risk score model, the HR of women in the 3^rd tertile was 6.78 (95% CI = 1.48–31.06) compared to patients in the 1^st tertile of polygenic risk score. Harrell’s C index was 0.813 with total patients and 0.924 in 4-fold cross validation. In the pathway analysis, 18 pathways were significantly associated with breast cancer prognosis (<i>P</i><0.1<i>)</i>. The <i>IL-6R</i>, <i>IL-8</i>, <i>IL-10RB</i>, <i>IL</i>-<i>12A</i>, and <i>IL</i>-<i>12B</i> was associated with the prognosis of cancer in data of both our study and a previous study. Therefore, our results suggest that genetic polymorphisms in immune-related genes have relevance to breast cancer prognosis among Korean women.</p></div

Additional file 2 of Oncologic outcomes after immediate breast reconstruction following mastectomy: comparison of implant and flap using propensity score matching

Additional file 2: Table S1. Patient demographics before propensity score matchin