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Lipophilic fluorescent dyes can be employed as sensors for surfactants present in concentrations above the critical micellar concentration (CMC) where the dyes are monodispersed in micelles. However, the surfactant concentration range over which these dyes are effective is narrowed because by the sigmoidal nature of their responses. To overcome this limitation, we developed a novel sensor material comprised of a labeled fluorescent solvatochromic dye covalently bonded to alginate gel, which is known to strongly adsorb cationic surfactants. We hypothesized that the dye-alginate conjugate would undergo fluorescent color changes in response to binding of surfactants which alter the polarity of the surrounding environment. Indeed, addition of the representative cationic surfactant, cetylpyridinium chloride (CPC), to an aqueous solution of the alginate conjugated fluorescent solvatochromic dye leads to a visible fluorescent color change when the concentration of CPC is below the CMC. The average values of the color appearance parameter, referred to as a hue, of light emitted from gels, calculated by analysis of fluorescence microscopy images using ImageJ software, were found to be approximately linearly dependent on the concentration of CPC encapsulated in the alginate-fluorescent dye complex. This finding shows that absorbed CPC can be quantitatively determined over a wide concentration range in the form of simple fluorescence wavelength or visible responses.

Ammonium and nitrate are the primary nitrogen sources in natural environments, and are essential for growth and development in photosynthetic eukaryotes. In this study, we report on the isolation and characterization of an ammonium transporter gene (PyAMT1) which performs a key function in nitrogen (N) metabolism of Pyropia yezoensis thalli. The predicted length of PyAMT1 was 483 amino acids (AAs). The AA sequence included 11 putative transmembrane domains and showed approximately 33–44% identity to algal and plant AMT1 AA sequences. Functional complementation in an AMT-defective yeast mutant indicated that PyAMT1 mediated ammonium transport across the plasma membrane. Expression analysis showed that the PyAMT1 mRNA level was strongly induced by N-deficiency, and was more highly suppressed by resupply of inorganic-N than organic-N. These results suggest that PyAMT1 plays important roles in the ammonium transport system, and is highly regulated in response to external/internal N-status.

We previously developed a 95‑gene classifier (95GC) to classify ER‑positive/HER2‑negative/node‑negative (ER+/HER2‑/N0) breast cancer as high‑ and low‑risk. The present study aimed to devise a 95GC recurrence score (95GCRS) to estimate recurrence risk more precisely and, although the 95GC was originally developed using fresh‑frozen (FF) tissues, this was applied to formalin‑fixed paraffin‑embedded (FFPE) tissues. 95GCRS was calculated using between‑group analysis and denominated as a value from 0 to 100. Correlation of 95GCRS with distant recurrence rate and response to neoadjuvant chemotherapy (NAC) was evaluated in 257 patients with ER+/HER2-/N0 breast cancer treated with adjuvant hormonal therapy at Osaka University Hospital and in 425 patients with ER+ breast cancer treated with NAC at Osaka University Hospital and the University of Texas MD Anderson Cancer Center (GSE25066 dataset). Correlation of 95GCRS between FF and FFPE tissues was evaluated in paired tissues from 56 ER+/HER2‑/N0 breast cancer types obtained from patients without NAC treatment. Distant recurrence rates were remarkably low in patients with 95GCRS ≤50 and increased proportionally in patients with 95GCRS >50. Pathological complete response (pCR) rates to NAC were increased in proportion to 95GCRS, indicating a greater sensitivity of breast cancers with high 95GCRS to chemotherapy. 95GCRS was highly correlated (R=0.92) between FF and FFPE tissues, and the concordance rate (94.6%) of high‑ and low‑risk groups was also considerably high. Overall, the present study developed a 95GCRS that correlated with distant recurrence rate and pCR rate to NAC. The 95GC was applicable to FFPE tissues with a high concordance rate in FF tissues.

Continued advancement of protein array, bioelectrode, and biosensor technologies is necessary to develop methods for higher amount and highly oriented immobilization activity of proteins. In pursuit of these goals, we developed a new immobilization method by combining electrostatic transport and subsequent molecular diffusion of protein molecules. Our developed immobilization method is based on a model that transports proteins toward the substrate surface due to steep concentration gradient generated by low-frequency AC electric field. The immobilization of the maximum amounts can be obtained by the application of the AC voltage of 80 Vpp, 20 Hz both for His-tagged Green Fluorescent Protein (GFP) and Discosoma sp. Red Fluorescent Protein (DsRed), used as model proteins. The amounts of the immobilized His-tagged GFP and DsRed were approximately seven-fold higher than that in the absence of the application of low-frequency AC electric field. Furthermore, the positively and negatively charged His-tagged GFP at acidic and alkaline pH were immobilized by applying of low-frequency AC electric field, whereas the non-charged His-tagged GFP at the pH corresponding to its isoelectric point (pI) was not immobilized. Therefore, unless the pH is equal to pI, the immobilization of electrically charged proteins was strongly enhanced through electrostatic transport and subsequent molecular diffusion.

Background Cancer microenvironment plays a vital role in cancer development and progression, and cancer-stromal interactions have been recognized as important targets for cancer therapy. However, identifying relevant and druggable cancer-stromal interactions is challenging due to the lack of quantitative methods to analyze whole cancer-stromal interactome. Results We present CASTIN (CAncer-STromal INteractome analysis), a novel framework for the evaluation of cancer-stromal interactome from RNA-Seq data using cancer xenograft models. For each ligand-receptor interaction which is derived from curated protein-protein interaction database, CASTIN summarizes gene expression profiles of cancer and stroma into three evaluation indices. These indices provide quantitative evaluation and comprehensive visualization of interactome, and thus enable to identify critical cancer-microenvironment interactions, which would be potential drug targets. We applied CASTIN to the dataset of pancreas ductal adenocarcinoma, and successfully characterized the individual cancer in terms of cancer-stromal relationships, and identified both well-known and less-characterized druggable interactions. Conclusions CASTIN provides comprehensive view of cancer-stromal interactome and is useful to identify critical interactions which may serve as potential drug targets in cancer-microenvironment. CASTIN is available at: http://github.com/tmd-gpat/CASTIN. Electronic supplementary material The online version of this article (doi:10.1186/s12864-016-3207-z) contains supplementary material, which is available to authorized users.

BACKGROUND: The aim of the present study was to investigate the prognostic value of the genomic grade index for lymph node-negative and estrogen receptor (ER)-positive breast cancers of Japanese women treated with adjuvant hormonal therapy alone, as well as the relation between genomic grade index and pathological complete response (CR) to neoadjuvant chemotherapy. METHODS: Genomic grade index was determined by DNA microarray (U133plus2.0; Affymetrix, Santa Clara, Calif) in tumor tissues obtained from lymph node-negative and ER-positive breast cancers (n = 105) treated with adjuvant hormonal therapy alone or in breast tumor biopsy specimens (n = 84, Mammotome) obtained before neoadjuvant chemotherapy (paclitaxel followed by 5-fluorouracil/epirubicin/cyclophosphomide) to investigate the prognostic and predictive values of genomic grade index. RESULTS: Recurrence-free survival of patients with high genomic grade index tumors was significantly (P < .001) lower than that of patients with low genomic grade index tumors (55% vs 88%, 10 years after surgery). Multivariate analysis demonstrated that genomic grade index was the most important and significant predictive factor for disease recurrence (P = .013) independently of other prognostic factors, including tumor size, histological grade, progesterone receptor, human epidermal growth receptor 2, and Ki67. High genomic grade index tumors showed a significantly (P = .022) higher pathological CR rate for neoadjuvant chemotherapy than low genomic grade index tumors (31.9% [15 of 47] vs 10.8% [4 of 37]). CONCLUSIONS: Genomic grade index is a powerful prognostic factor for lymph node-negative and ER-positive tumors treated with adjuvant hormonal therapy alone, and high genomic grade index tumors are more likely to respond to chemotherapy. Genomic grade index also appears to be very useful for decision making regarding the need for adjuvant chemotherapy for lymph node-negative and ER-positive breast cancers. Cancer 2011. © 2010 American Cancer Society.

e12107 Background: Curebest™ 95GC Breast (95GC) is one of the multi-gene assays to predict prognosis of node negative and estrogen receptor (ER) - positive breast cancer patients, developed using 95 gene-set without overlap with that used in Oncotype DXⓇ(ref 1). It has been shown to have the capability to classify the “intermediate” patients determined using Recurrence Online (microarray-based simulation model for Oncotype DXⓇ) but was validated only using the data from single institute and public database. Here we report the result of the first multi-center validation study for this multi-gene assay. Methods: ER-positive and T1-2/N0/M0 breast cancer patients who received adjuvant hormonal therapy were enrolled retrospectively. Fresh frozen tissues were applied to the assay, resulting classification into “L” and “H”, which was used for the validation on 5 year recurrence free survival (5Y-RFS) data of each patient. Results: 73 cases out of 150 enrolled cases were eligible and analyzed. 46 patients were classified as “L” whose 5Y-RFS was 96.5% (95%CI:89.5-98.9) while 27 patients were classified as “H” whose 5Y-RFS was 79.0% (95%CI:63.6-88.5). There was a statistically significant difference between RFS of “L” and “H” group by Log-Rank test (p = 0.0016). Significant association with 95GC were seen in histological grade (p = 0.0012), Recurrence Online (p < 0.001) and PAM50 (p < 0.001). The assay could classify the patients of histological grade 2, intermediate group by Recurrence Online (RS > 17, RS < 31) and Luminal B patients into “L” and “H”. Conclusions: Curebest™ 95GC Breast was well validated by this first multi-centered retrospective study on 5Y-RFS of the ER positive, node-negative patients who received only hormonal therapy in adjuvant setting. This result indicates the usefulness of 95GC as a novel multi-gene assay, as it can classify target patients into 2 groups, “H” and “L” according to predicted prognosis of 5Y-RFS. Reference: 1. Naoi et al. Breast Cancer Res Treat (2011) 128:632-641

Background The aim of this study was to construct a novel prediction model for the pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) using immune-related gene expression data. Patients and methods DNA microarray data were used to perform a gene expression analysis of tumor samples obtained before NAC from 117 primary breast cancer patients. The samples were randomly divided into the training (n = 58) and the internal validation (n = 59) sets that were used to construct the prediction model for pCR. The model was further validated using an external validation set consisting of 901 patients treated with NAC from six public datasets. Results The training set was used to construct an immune-related 23-gene signature for NAC (IRSN-23) that is capable of classifying the patients as either genomically predicted responders (Gp-R) or non-responders (Gp-NR). IRSN-23 was first validated using an internal validation set, and the results showed that the pCR rate for Gp-R was significantly higher than that obtained for Gp-NR (38 versus 0%, P = 1.04E-04). The model was then tested using an external validation set, and this analysis showed that the pCR rate for Gp-R was also significantly higher (40 versus 11%, P = 4.98E-23). IRSN-23 predicted pCR regardless of the intrinsic subtypes (PAM50) and chemotherapeutic regimens, and a multivariate analysis showed that IRSN-23 was the most important predictor of pCR (odds ratio = 4.6; 95% confidence interval = 2.7–7.7; P = 8.25E-09). Conclusion The novel prediction model (IRSN-23) constructed with immune-related genes can predict pCR independently of the intrinsic subtypes and chemotherapeutic regimens.

We recently developed a 95-gene classifier (95GC) for the prognostic prediction for ER-positive and node-negative breast cancer patients treated with only adjuvant hormonal therapy. The aim of this study was to validate the efficacy of 95GC and compare it with that of 21GC (Oncotype DX) as well as to evaluate the combination of 95GC and 21GC. DNA microarray data (gene expression) of ER-positive and node-negative breast cancer patients (n = 459) treated with adjuvant hormone therapy alone as well as those of ER-positive breast cancer patients treated with neoadjuvant chemotherapy (n = 359) were classified with 95GC and 21GC (Recurrence Online at http://www.recurrenceonline.com/ ). 95GC classified the 459 patients into low-risk (n = 285; 10 year relapse-free survival: 88.8 %) and high-risk groups (n = 174; 70.6 %) (P = 5.5e−10), and 21GC into low-risk group (n = 286; 89.3 %), intermediate-risk (n = 81; 75.7 %), and high-risk (n = 92; 64.7 %) groups (P = 2.9e−10). The combination of 95GC and 21GC classified them into low-risk (n = 324; 88.9 %) and high-risk (n = 135; 65.0 %) groups (P = 5.9e−14), and also showed that pathological complete response rates were significantly (P = 2.5e−6) higher for the high-risk (17.9 %) than the low-risk group (3.6 %). In addition, we demonstrated that 95GC was calculated on a single-sample basis if the reference robust multi-array average workflow was used for normalization. The prognostic prediction capability of 95GC appears to be comparable to that of 21GC. Moreover, their combination seems to result in the identification of more low-risk patients who do not need chemotherapy than either classification alone. The patients in the high-risk group were found to be more chemo-sensitive so that they can benefit more from adjuvant chemotherapy.

A 95-gene classifier (95-GC) recently developed by us can predict the risk of relapse for ER-positive and node-negative breast cancer patients with high accuracy. This study investigated association of risk classification by 95-GC with response to neoadjuvant chemotherapy (NAC). Tumor biopsy samples obtained preoperatively from 72 patients with ER-positive breast cancer were classified by 95-GC into high-risk and low-risk for relapse. Pathological complete response (pCR) rate was numerically higher for high-risk (15.8%) than low-risk patients (8.8%) although the difference was not statistically significant. Pathological response evaluated in terms of the pathological partial response (pPR) rate (loss of tumor cells in more than two-thirds of the primary tumor) showed a significant association (P = 0.005) between the high-risk patients and a high pPR rate. Besides, external validation study using the public data base (GSE25066) showed that the pCR rate (16.4%) for high-risk patients (n = 128) was significantly (P = 0.003) higher than for low-risk patients (5.7%) (n = 159). These results demonstrate that the high-risk patients for relapse show a higher sensitivity to chemotherapy and thus are likely to benefit more from adjuvant chemotherapy.

Association of estrogen receptor (ER), progesterone receptor (PR), HER2, Ki67 and 70-gene classifier (70-GC) with a response to paclitaxel (PAC) (n=79) or docetaxel (DOC) (n=55) was investigated in the neoadjuvant setting for breast cancer patients. Sensitivity of breast tumors to PAC, but not to DOC, was found to be significantly associated with ER negativity (P=0.003), PR negativity (P=0.007), and Ki67 positivity (P=0.007). Breast tumors classified into the responders by 70-GC showed a significantly (P=0.005) higher reduction rate to PAC and interestingly a significantly (P=0.009) lower reduction rate to DOC than those classified into the non-responders by 70-GC, suggesting that 70-GC might be useful for the differentiation of PAC-sensitive and DOC-sensitive breast tumors.

Background The aim of this study was to investigate whether c-myc amplification in human breast cancer is associated with response to neoadjuvant chemotherapy comprising paclitaxel followed by 5-FU/epirubicin/cyclophosphamide (P-FEC). Methods Tumour tissue samples were obtained before neoadjuvant chemotherapy (P-FEC) from 100 primary breast cancer patients (stage II/III). C-myc and HER2 amplification were examined by FISH, and oestrogen receptor (ER), progesterone receptor (PR), Ki67, and topoisomerase 2α (TOP2A) expression were examined immunohistochemically. Pathological complete response (pCR) was defined by a complete loss of tumour cells in the breast without any lymph node metastasis. Results C-myc amplification was observed in 40% (40/100) of breast tumours, and was significantly associated with ER-negative tumours (23/40 for ER(–) versus 17/60 for ER(+), P = 0.004), high histological grade tumours (11/18 for grade 3 versus 29/82 for grades 1 + 2, P = 0.043) and TOP2A-positive tumours (28/51 for TOP2A(+) versus 12/49 for TOP2A(–), P = 0.002). pCR rate was 20% for total patients (10.0% for ER(+) and 35.0% for ER(–)). Further, breast tumours with c-myc amplification (c-myc(+)) showed a significantly (P = 0.041) higher pCR rate (12/40) than those without such amplification (c-myc(–)) (8/60). This association between pCR and c-myc amplification was observed in ER-positive tumours (4/17 for c-myc(+) versus 2/43 for c-myc(–), P = 0.048) but not in ER-negative tumours (8/23 for c-myc(+) versus 6/17 for c-myc(–), P = 0.973). Conclusion Our results suggest that c-myc amplification is significantly associated with a high pCR rate to P-FEC in breast tumours, especially in ER-positive tumours.

Purpose The aim of this study was to determine whether TP53 mutation status (MS) can predict response of breast cancer to paclitaxel followed by 5-FU/epirubicin/cyclophosphamide (P-FEC). TP53 gene expression signature (GES) was also examined for its predictive capability of response to P-FEC since TP53 GES provides a more accurate measure of the functional configuration of TP53. Methods Tumor samples were obtained from 72 primary breast cancer patients (stage II/III) before neoadjuvant chemotherapy (P-FEC) and analyzed for identification of TP53 MS (genomic sequencing), TP53 GES (DNA microarray), and p53 protein expression (immunohistochemistry). Results Of 72 breast tumors, 16 were TP53 mutant-type (TP53 mt) and 56 were wild-type (TP53 wt). 29 tumors (40%) were positive for p53 protein by immunohistochemistry. DNA microarray analysis showed that 27 were TP53 mt-like tumors and 45 were TP53 wt-like tumors, depending on the expression signature of the TP53-related 31-genes. There was no statistically significant difference in pathological complete response (pCR) rates between TP53 mt and wt tumors (19% vs 23%) and between p53 positive and negative tumors (24% vs 21%) but TP53 mt-like tumors showed a significantly (P = 0.019) higher pCR rate (37%) than TP53 wt-like tumors (13%) (Hazard ratio, 3.82; 95% C.I., 1.20–12.21). Conclusions TP53 GES, but not TP53 MS and p53 protein expression, is predictive of response to neoadjuvant P-FEC, suggesting that TP53 GES more correctly reflects the functionality of TP53.

BACKGROUND. Sequential administration of paclitaxel plus combined fluorouracil, epirubicin, and cyclophosphamide (P-FEC) is 1 of the most common neoadjuvant chemotherapies for patients with primary breast cancer and produces pathologic complete response (pCR) rates of 20% to 30%. However, a predictor of pCR to this chemotherapy has yet to be developed. The authors developed such a predictor by using a proprietary DNA microarray for gene expression analysis of breast tumor tissues. METHODS. Tumor samples were obtained from 84 patients with breast cancer by core-needle biopsy before the patients received P-FEC, and the gene expression profile was analyzed in those samples to construct a classifier for predicting pCR to P-FEC. In addition, the authors analyzed the gene expression profile of tumor tissues that were obtained at surgery from 105 patients with lymph node-negative and estrogen receptor-positive breast cancer who received adjuvant hormone therapy alone to determine the prognostic significance of the classifier. RESULTS. The 70-gene classifier for predicting pCR to P-FEC was constructed by using the training set (n = 50) and subsequently was validated successfully in the validation set (n = 34), revealing high sensitivity (88%; 95% confidence interval [CI], 47%-100%) and high negative predictive value (93%; 95% CI, 68%-100%). Specificity and positive predictive value were 54% (95% CI, 33%-73%) and 37% (95% CI, 16%-62%), respectively. Among the various parameters (estrogen receptor, progesterone receptor, human epidermal growth factor receptor 2, and Ki-67 status, etc), the 70-gene classifier had the strongest association with pCR (P = .015). In an additional study, genetically assumed complete responders were associated significantly (P = .047) with a poor prognosis. CONCLUSIONS. The 70-gene classifier that was constructed for predicting pCR to P-FEC for breast tumors was successful, with high sensitivity and high negative predictive value. The classifier also appeared to be useful for predicting the prognosis of patients with lymph node-negative and estrogen receptor-positive breast cancer who receive adjuvant hormone therapy alone. Cancer 2011. © 2011 American Cancer Society.

Our aim was to develop an accurate diagnostic system using gene expression analysis by means of DNA microarray for prognosis of node-negative and estrogen receptor (ER)-positive breast cancer patients in order to identify a subset of patients who can be safely spared adjuvant chemotherapy. A diagnostic system comprising a 95-gene classifier was developed for predicting the prognosis of node-negative and ER-positive breast cancer patients by using already published DNA microarray (gene expression) data (n = 549) as the training set and the DNA microarray data (n = 105) obtained at our institute as the validation set. Performance of the 95-gene classifier was compared with that of conventional prognostic factors as well as of the genomic grade index (GGI) based on the expression of 70 genes. With the 95-gene classifier we could classify the 105 patients in the validation set into a high-risk (n = 44) and a low-risk (n = 61) group with 10-year recurrence-free survival rates of 93 and 53%, respectively (P = 8.6e-7). Multivariate analysis demonstrated that the 95-gene classifier was the most important and significant predictor of recurrence (P = 9.6e-4) independently of tumor size, histological grade, progesterone receptor, HER2, Ki67, or GGI. The 95-gene classifier developed by us can predict the prognosis of node-negative and ER-positive breast cancer patients with high accuracy. The 95-gene classifier seems to perform better than the GGI. As many as 58% of the patients classified into the low-risk group with this classifier could be safely spared adjuvant chemotherapy.