Ryo Tsunashima

Prediction of pathologic complete response to sequential paclitaxel and 5-fluorouracil/epirubicin/cyclophosphamide therapy using a 70-gene classifier for breast cancers.

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.

Construction of novel immune-related signature for prediction of pathological complete response to neoadjuvant chemotherapy in human breast cancer

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.

High genomic grade index associated with poor prognosis for lymph node‐negative and estrogen receptor‐positive breast cancers and with good response to chemotherapy

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.

Estrogen receptor positive breast cancer identified by 95-gene classifier as at high risk for relapse shows better response to neoadjuvant 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.

Development of a prediction model for lymph node metastasis in luminal A subtype breast cancer: The possibility to omit sentinel lymph node biopsy

The present study aimed to construct a prediction model for axillary lymph node metastasis (ALNM) using a DNA microarray assay for gene expression in breast tumor tissues. Luminal A breast cancers, diagnosed by PAM50 testing, were analyzed, and a prediction model (genomic nodal index (GNI)) consisting of 292 probe sets for ALNM was constructed in a training set of patients (n=388), and was validated in the first (n=59) and the second (n=103) validation sets. AUCs of ROC were 0.820, 0.717, and 0.749 in the training, first, and second validation sets, respectively. GNI was most significantly associated with ALNM, independently of the other conventional clinicopathological parameters in all cohorts. It is suggested that GNI can be used to identify the patients with a low risk for ALNM so that sentinel lymph node biopsy can be spared safely.

70-Gene classifier for differentiation between paclitaxel- and docetaxel-sensitive breast cancers

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.

Construction of multi-gene classifier for prediction of response to and prognosis after neoadjuvant chemotherapy for estrogen receptor positive breast cancers

The aims of this study were to develop a multi-gene expression-based prediction model for pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) and to evaluate its prognosis prediction for estrogen receptor (ER) positive breast cancers. The training set included the NAC-treated patients (n = 104) with ER+ breast tumors in our hospital and the validation set included the NAC-treated patients (n = 259) with ER+/HER2- breast tumors in the public database (GSE25066). Gene expression in the tumor biopsy specimens obtained before NAC was analyzed with DNA microarray, and the prediction model (MPCP155) for pCR was constructed for the training set by using the genes (155 probes) involved in the metabolic pathways which the pathway analysis identified as being significantly associated with pathological response. With MPCP155, the tumors in the validation set could be classified into low chemo-sensitive (low-CS) (pCR rate = 2.6%) and high-CS (pCR rate = 15.3%; P = 0.0006) groups. Furthermore, the low-CS group showed a significantly better prognosis than the high-CS group (P = 2.0E-6). Moreover, prognosis prediction by MPCP155 was independent of the residual cancer burden score. MPCP155 may be helpful for decision making regarding the indication for neoadjuvant chemotherapy. In addition, MPCP155 was found to be useful for prognosis prediction for NAC-treated patients with ER+/HER2- tumors.

Lack of genomic rearrangements involving the aromatase gene CYP19A1 in breast cancer

Increased intratumoral expression of aromatase, the key enzyme for estrogen biosynthesis, is predicted to be of critical importance in the development of breast cancer. Recently, several germline rearrangements at 15q21 have been shown to cause overexpression of the aromatase gene CYP19A1 and resulting aromatase excess syndrome. To determine whether submicroscopic genomic rearrangements at 15q21 are involved in aromatase overexpression in breast cancer tissues, we investigated copy-number alterations in genomic DNA obtained from 44 tumor samples. Comparative genomic hybridization analysis identified no deletion or duplication at 15q21 in the 44 samples. These results, in conjunction with previous data, indicate that aromatase overexpression in breast cancer tissues is likely to result from a promoter switch of CYP19A1 and/or accumulation of CYP19A1-expressing cells, rather than from cryptic transactivation of CYP19A1 because of genomic rearrangements at 15q21.

Endocrine sensitivity of estrogen receptor-positive breast cancer is negatively correlated with aspartate-β-hydroxylase expression.

Although prognostic markers for early estrogen receptor (ER)‐positive breast cancer have been extensively developed, predictive markers for adjuvant endocrine therapy are still lacking. Focusing on the mechanisms underlying endocrine resistance, we investigated whether the endocrine sensitivity of ER‐positive breast cancer cells was correlated with the expression of aspartate‐β‐hydroxylase (ASPH), which is involved in the development of hepatocellular carcinoma. ASPH expression in ER‐positive and tamoxifen‐resistant breast cancer cells was upregulated by the MAPK and phosphoinositide‐3 kinase (PI3K) pathways, which both play pivotal roles in endocrine resistance. In the clinical setting, ASPH expression was negatively correlated with recurrence‐free survival of luminal B breast cancer patients that received adjuvant endocrine therapy, but not in patients that did not receive adjuvant endocrine therapy. Luminal B breast cancer is one of the intrinsic molecular subtypes identified by the Prediction Analysis of Microarray 50 (PAM50) multiple gene classifier, and because of its poor response to endocrine therapy, chemotherapy in addition to endocrine therapy is generally required after surgical resection. Our results suggest that the endocrine sensitivity of luminal B breast cancer can be assessed by examining ASPH expression, which promotes the consideration of a prospective study on the association between ASPH expression at the mRNA and protein levels in luminal B breast cancer and subsequent response to endocrine therapy.