Research Article

Biomarker Alteration to Neoadjuvant Chemotherapy Predict Pathological Response and Prognosis in Breast Cancer Patients  

Yue Zhao1 , Dongwei Zhang2, 3
1 Department of Breast Surgery, Harbin Medical University Cancer Hospital, Harbin, Heilongjiang, China
2 Department of Surgery, the Second Affiliated of Harbin Medical University, Harbin, Heilongjiang, China
3 Heilongjiang Academy of Medical Sciences, Harbin, Heilongjiang, China
Author    Correspondence author
Cancer Genetics and Epigenetics, 2018, Vol. 6, No. 1   doi: 10.5376/cge.2018.06.0001
Received: 29 Mar., 2018    Accepted: 09 Apr., 2018    Published: 18 May, 2018
© 2018 BioPublisher Publishing Platform
This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Preferred citation for this article:

Zhao Y., and Zhang D.W., 2018, Biomarker alteration to neoadjuvant chemotherapy predict pathological response and prognosis in breast cancer patients, Cancer Genetics and Epigenetics, 6(1): 1-12 (doi: 10.5376/cge.2018.06.0001)

Abstract

Background: The values of biomarkers expression might be changed following neoadjuvant chemotherapy (NACT), but little is known about the change range and its relationship to prognosis. This study aimed to investigate the potential changes of biomarkers expression before and after neoadjuvant chemotherapy, then predicting the pathological response and prognosis to NACT. Methods: A total of 119 patients who were initially diagnosed of breast cancer and underwent neoadjuvant chemotherapy were included in the study. Miller-Payne grading system was used to evaluate the pathologic response after neoadjuvant chemotherapy. Survival curves were estimated using the Kaplan-Meier method, and the log-rank test was used to test for differences between groups. Results: The high expression of ER, PR and Ki67 pre-NACT, the biomarkers expression post-NACT is also high (All P values <0.05). We found that the change of biomarkers expression before and after chemotherapy were all considered as medium changes (range between 10 to 30), while only PR expression change after NACT were associated with distant disease-free survival (P<0.001) and overall survival (P=0.031,6). PR expression also related to pathologic response (P=0.028) but not ER, HER2 and Ki-67. Furthermore, a total of 67 down regulated of Ki67 expression compared with 37 up regulated expression, the results showed that decreasing expression of Ki67 had fewer local recurrence compared with Ki67 increasing expression after NACT. Conclusions: Our research have provided the prognostic value of biomarkers expression change following the neoadjuvant chemotherapy. These findings might help optimize the choice of targeted therapy and improve the predictive effect to patient survival.

Keywords
Breast cancer; Neoadjuvant chemotherapy; Biomarkers; Expression; Prognosis

Background

Breast cancer is a clinically heterogeneous disease and the most commonly diagnosed cancer in women. Most patients are diagnosed in the middle or late stages because no typical symptoms for the early stage of breast cancer exist and is the leading cause of cancer related death amongst women worldwide (Parkin et al., 2005). Neoadjuvant chemotherapy (NACT) is often used in the treatment of patients with locally advanced breast cancers (LABC), large operable breast cancer or proven lymph node metastasis (Wolff and Davidson, 2000; Cance et al., 2002; Colleoni and Goldhirsch, 2014; Read et al., 2015). The advantage of NACT is to downstage the tumor load to increase the rate of breast conserving surgery and to gain information on drug response by breast assessment (Thompson and Moulder-Thompson, 2012).

 

Miller and Payne classification estimates tumor response to primary chemotherapy according to the decrease of cancer cellularity after treatment, in patients with large and LABC (Ogston et al., 2003). Pathologic complete response (pCR) after neoadjuvant chemotherapy has been described as a strong indicator of survival, justifying its use as a surrogate marker of chemosensitivity (Kurosumi, 2004). Patients who achieve a pathological complete response have a better prognosis than those who do not, but are achieved in only 10%-20% of cases (Rastogi et al., 2008). Predictive markers of pathologic complete response such as estrogen receptor (ER) status, HER-2, vascular space invasion, and tumor necrosis factor have been the focus of numerous studies over the years (Davis et al., 2003; Ganem et al., 2003; Stearns et al., 2003; Amat et al., 2005). However, the results of these studies are not concordant, indicating that the clinical and prognostic value of those markers might not be fully understood (Ko et al., 2013).

 

Breast cancer can be divided into distinct molecular subtypes by immunohistochemical of estrogen receptor (ER), progesterone receptor (PR), HER-2 and Ki67. Similarly tumors may have different prognosis because of molecular subtypes and chemosensitivity differently (Hugh et al., 2009). Triple-negative breast cancer has the characteristics of aggressive clinical behavior, rapid growth and a poor prognosis, but is the molecular subtype most sensitive to NACT (Carey et al., 2007; Cheang et al., 2008). Luminal A tumors are considered to have high expression of hormone receptors and low expression of Ki-67, indicating that they have a good prognosis, but they are less sensitive to chemotherapy. The status of ER, PR, HER2 and Ki-67 can alter after neoadjuvant chemotherapy in previous studies (Shet et al., 2007; Jin et al., 2015). However, the level alteration of biomarker after neoadjuvant chemotherapy might impact of prognosis because of tumor heterogeneity, and little information is available on it. The aim of our study was to evaluate pathologic response and prognosis through the level change of ER, PR, HER2 and Ki-67 following neoadjuvant chemotherapy, and screened the highest amplitude of variation from predictive biomarker for further research.

 

1 Results

1.1 Patient characteristics and survival

A total of 119 patients who underwent neoadjuvant chemotherapy were included in the study. The median age of patients was 47 years (range: 25-69 years). The median follow-up time was 49 months, with 31 deaths, 56 distant metastases and 52 local recurrences. All patients were diagnosed with stage II or III disease at diagnosis. Of the 119 patients, 27 patients (22.69%) were younger than 40 years and majority patients (48.74%) were in 40 years to 55 years. Patient age for this cohort was statistically significant for distant disease-free survival, local recurrence-free survival (All P values <0.05). Patients whose ages are less than 40 have poorer prognosis compared with patients of ages older than 40. A total of 15 of the 119 patients (12.6%) with primary breast cancer who received NACT in our study were considered pathologic complete response (pCR). Miller-Payne grading were associated with distant disease-free survival and overall survival, the higher Miller-Payne grading related to less distant metastases and deaths (P<0.05). The results compared Ki67 with survival were showed that higher Ki67 after surgery in the prediction of more distant metastases. Tumor size and lymph nodes positivity before and after NACT were associated with survival (P<0.05). However, tumor size and lymph nodes post-NACT correlated with distant disease-free survival, tumor size pre-NACT were associated with local recurrence free survival and lymph nodes pre-NACT related to overall survival (Table 1).

 

Table 1 Clinical and pathological characteristics of patients

 

1.2 Biomarker expression and pathologic response

Miller-Payne grading (MPG) system was used to evaluate the pathologic response of all patients after neoadjuvant chemotherapy. The results of the relationship between biomarker expression and pathologic response were showed in the analysis (Table 2). Remarkably, pathologic response were associated with PR (P=0.028) but not ER, HER2 and Ki-67. A total of 13 of the 15 patients (86.67%) who received NACT arrived pathologic complete response were PR negative in our study (Table 2).

 

Table 2 Biomarker expression pre-NACT and pathologic response

 

Next we focus on cases with biomarker expression status alterations after NACT. Patients without significant changes of biomarker expression status pre-NACT and post-NACT were excluded from our study, with 69 ER changes, 64 PR changes and 104 Ki67 changes. The results of biomarker expression changes showed that more down expression than up expression after NACT, with 22 up regulated expression and 47 down regulated expression of ER status, 16 up regulated expression and 48 down regulated expression of PR status, 37 up regulated expression and 67 down regulated expression of Ki67 status (Figure 1). After that we explored two groups with biomarker up and down expression post-NACT to compare the relationship between it and survival. As Ki67 down regulated expression was strongly associated with local recurrence free survival, lower Ki67 expression post-NACT had fewer local recurrence.

 

Figure 1 Kaplan-Meier survival curves for distant disease free survival (DDFS), local recurrence free survival (LRFS) and overall survival (OS) according to change direction of biomarkers expression between pre and post-NACT

Note: (A) The survival of ER up-regulated compared to ER down-regulated; (B) The survival of PR up-regulated compared to PR down-regulated; (C) The survival of Ki67 up-regulated compared to Ki67 ER down-regulated

 

The clinical-pathologic characteristics and biomarker expression status are summarized in Table 3. The tumor size after neoadjuvant chemotherapy was associated with PR pre-NACT, PR post-NACT and Ki67 post-NACT. Lymph node status was correlated to ER status. In terms of lymph node status, we found a significant correlated to ER expression status no matter before and after NACT. We also found PR expression status before NACT was one of the determined factors to axillary node metastasis.

 

Table 3 Clinical characteristics according to biomarker expression pre and post NACT

 

Patients with ER, PR and ki67 different amplitude of variation before and after NACT were divided into four groups (Figure 2): cha=0 (no change), cha<10 (low change), 10≤cha≤30 (medium changes), cha>30 (high changes). Patients with biomarker negative expression both pre-NACT and post-NACT were excluded from this study. As predicted, we found that the changes of biomarkers expression before and after chemotherapy were all mainly focused on the range between 10 and 30 (Figure 2). According to further compared the expression range with survival, biomarkers expression change of all patients were categorized as low changes (<10), medium changes (10-30), and high changes (>=30), respectively (Table 4; Table 5; Table 6). Among the classification schemes evaluated, only PR expression change predicted DDFS and OS in our cohort (Table 5). The increasing PR expression change were interrelated predictors of a worse distant disease-free survival and overall survival.

 

Figure 2 The change range of biomarkers expression before and after NACT

Note: (A) The change range to ER expression; (B) The change range to PR expression; (C) The change range to Ki67 expression

 

Table 4 The change range of ER expression and outcome

 

Table 5 The change range of PR expression and outcome

 

Table 6 The change range of Ki67 expression and outcome

 

2 Discussion

Neoadjuvant chemotherapy is a well-established approach to treatment of locally advanced breast cancer and offers several clinical advantages. Gene-expression profiling has had a considerable impact on our understanding of breast cancer biology, and more recently on clinical care. Several studies have suggested that the expression of biomarker is changed after NACT and is critical for evaluation of NACT efficiency (van de Ven et al., 2011; Chen et al., 2012). The molecular subtypes of breast cancer, which are increasingly used for predicting the pathological response to NACT, are related to histological grade and proliferative activity (Tordai et al., 2008; Parker et al., 2009). Changes of molecular subtypes in breast cancer after NACT which include 4 biomarkers have been described in the literature (Zhao et al., 2015). Few prospective studies have focused on the prognostic value of a single biomarker expression changes to prognosis. This study highlights the importance of the biomarker expression changes in the prognosis of breast cancer and the interaction between clinical characteristics and biomarker expression status in this setting.

 

It is now accepted that pathological assessment is the gold standard for evaluation of NACT response and determination of residual cancer burden. In our prospective observational study, we demonstrated the higher expression of ER, PR and Ki67 post-NACT correlated with the higher biomarker expression pre-NACT in most patients. Then we get rid of patients whose biomarker negative expression both pre-NACT and post-NACT. The results are also showed that the expression of ER, PR, Ki-67 a significant positive correlation between pre-NACT and post-NACT, namely of the index before chemotherapy is higher, after chemotherapy index is relatively higher. The expression of ER, PR and Ki67 pre-NACT may reflect a relatively response to chemotherapy and may be associated with the expression of ER, PR and Ki67 post-NACT.

 

The differences in the clinical and pathologic characteristics observed by biomarker expression before and after NACT have been compared in Table 3. The tumor size after neoadjuvant chemotherapy was associated with PR pre-NACT, PR post-NACT and Ki67 post-NACT. Lymph node status was correlated to ER status. In terms of lymph node status, we found a significant correlated to ER expression status no matter before and after NACT. We also found PR expression status before NACT was one of the determined factors to axillary node metastasis. Therefore, tumor size after NACT and lymph node status were partly associated with the biomarker expression before and after NACT.

 

As we all known that patients with the triple negative subtype were more likely to achieve pathologic complete response than other molecular subtypes of breast cancer. Several approaches have been undertaken to differentiate ER+ and PR+/PR- tumors with different chemosensitivity and survival (Feeley et al., 2014). MPG as predictive factor in relation to neoadjuvant chemotherapy and important effects on prognosis (Table 1). Then the results of the relationship between biomarker expression and pathologic response were showed that pathologic response associated with PR expression (P=0.028) but not ER, HER2 and Ki-67 (Table 2). A total of 13 of the 15 patients (86.67%) who received NACT arrived pathologic complete response were PR negative in our study (Table 2). In our study, we mainly analyzed the difference value of biomarker expression changes between before and after NACT. As predicted, we found that the change range of biomarker expression before and after chemotherapy were all mainly between 10 and 30 which we considered as medium changes (Figure 2). The PR is a downstream relative of the ER, which activates the expression of PR via the estrogen-responsive element located in the promoter region of the PR gene. The loss of PR may be a surrogate marker of a nonfunctional ER, and it also probably gains limited benefit from endocrine therapy (Rakha et al., 2007; Davies et al., 2011). According to further compared the biomarker expression range correlation with survival, only PR expression change predicted DDFS and OS in our cohort (Table 5). The higher difference PR expression change after NACT indicated worse distant disease-free survival and overall survival, while the patients with PR high expression before NACT and changed to PR negative expression after NACT considered as worst prognosis. Therefore, PR successed to show prognostic value in pathologic response and prognosis in this study.

 

Many retrospective studies investigated the value of Ki67 as a predictive factor in relation to neoadjuvant chemotherapy (Sueta et al., 2014), but the debate on the prognostic role of Ki67 in breast cancer is still open. In the present study, we assessed Ki67 expression before and after neoadjuvant chemotherapy in primary breast cancer patients revealed that Ki67 post-NACT adds independent prognostic information to survival. Patients with high Ki67 levels after NACT showed higher risk for distant metastases compared with patients with low Ki67 level (Table 1), but Ki67 levels before NACT was not associated with survival prediction. The result of correlation between clinical characteristics and biomarker expression analysis showed that post-NACT Ki67 expression was significantly associated with tumour size post-NACT. Ki67 post-NACT as a predictive factor to prognosis, its expression was associated with the Ki67 pre-NACT expression. The majority of patients can benefit from neoadjuvant chemotherapy, and more down expression than up expression after NACT. These results might suggest that the Ki67 cut-off values in patients undergoing chemotherapy to allow prediction of local recurrence free survival (Figure 1), namely decreasing expression of Ki67 had fewer local recurrence compared with Ki67 increasing expression after NACT. We speculate that further clinically significant observations might be made in identifying a subpopulation among those with Ki67 down regulated tumours.

 

3 Materials and Methods

3.1 Patients

This study was approved by the institutional review board (IRB) of Harbin Medical University. One hundred and nineteen patients who were initially diagnosed of breast cancer between 2007 and 2014 by core needle biopsy and treated with neoadjuvant chemotherapy followed by surgical resection were retrieved from the Second Affiliated Hospital, Harbin Medical University. Patients who had any treatment prior to NACT were not eligible for this study. Information on the following parameters had to be available from the pretreatment assessment: patient’s age, tumor size, lymph nodes, estrogen receptor status, progesterone status, HER2 status, grading, and proliferation status as assessed by Ki67 staining. Tumor size was measured by ultrasound. The tumor size and the lymph nodes were assessed with using the 7th edition of the American Joint Committee on Cancer (AJCC) staging manual pre-NACT and post-NACT (Edge et al., 2010).

 

The NACT regimens contained docetaxel, epirubicin and/or cyclophosphamide (TEC: T 75 mg/m2 iv d1, E 50 mg/m2 iv d1 and C 500 mg/m2 iv d1, repeated every 21 days); docetaxel, carboplatin and trastuzumab/docetaxel plus trastuzumab (TCH: T 75 mg/m2 iv d1, Cb AUC = 6 iv d1, H 6mg/kg iv d1, repeated every 21 days/TH: T 75 mg/m2 iv d1, H 6 mg/kg iv d1, repeated every 21 days); and docetaxel plus cyclophosphamide (TC: T 75 mg/m2 iv d1, C 600 mg/m2 iv d1, repeated every 21 days). All patients received at least 4 to 6 cycles of NACT before surgery. The type of surgical procedure depended on residual tumour size and patient preference. All patients received breast conservation surgery/mastectomy with axillary lymph node dissection. The patients of ER/PR+ received endocrine therapy and the positive of axillary lymph node following NACT were accepted radiation therapy.

 

Miller-Payne grading (MPG) system was used to evaluate the pathologic response after neoadjuvant chemotherapy. The response to NACT was assessed by two pathologists independently according to Miller-Payne grading system. Miller-Payne grading provides a 5-step scale based on tumor cellularity in the excision/mastectomy specimen as compared with the pretreatment core biopsy, Grade 1: No reduction in overall cellularity; Grade 2: Minor (<30%) loss of cellularity; Grade 3: Between an estimated 30% and 90% reduction in tumor cells; Grade 4: more than 90% loss of tumor cells; Grade 5: No invasive carcinoma; ductal carcinoma in situ (DCIS) may be present (Ogston et al., 2003). Some patients without Miller-Payne grading were retested by two pathologists.

 

3.2 Immunohistochemistry and molecular subtypes

Estrogen receptor (ER), progesterone receptor (PR), HER2 neu and Ki-67 Immunohistochemical assay (IHC) was performed according to the standard diagnostic protocol. The cut-off values for ER positivity was defined as ≥1% tumor cells with nuclear staining and PR positivity was defined as ≥20% of tumor cells with nuclear staining. The IHC staining for HER2 was scored according to standard criteria as 0, 1+, 2+, or 3+ (Sauter et al., 2009). Scores of 0 and 1+ were considered of negative and 3+ was considered HER2-positive. When a score of 2+ was found, additional fluorescence in situ hybridization (FISH) testing was done to establish HER2 gene amplification status. A positive result was defined as an HER2 gene/chromosome 17 ratio of larger than 2.0. The ki-67 positive was defined as ≥14% and negative was defined as <14% (Cheang et al., 2009). The subtype was proposed to separate luminal A (ER+ and PR+, HER2-, Ki-67 <14%), luminal B (ER+ and/or PR+, HER2-; ER+ and/or PR+, HER2-, Ki-67 ≥14%; ER+ and/or PR+, HER2+), HER2-enriched (ER-, PR-, HER2+), and triple-negative (ER-, PR-, HER2-) (Goldhirsch et al., 2013).

 

3.3 Statistical analysis

Distant disease-free survival (DDFS) was defined as the time interval between surgery and the first documented distant relapse, death, or last follow-up. Local recurrence-free survival (LRFS) was defined as the time interval between surgery and the first documented local recurrence, death, or last follow-up. Overall survival (OS) was defined as the time between surgery and death or last follow-up, whichever occurred first. Statistical analyses were performed using the R programming language, Survival curves were estimated using the Kaplan-Meier method, and the log-rank test was used to test for differences between groups. Graphs Prism 5 used for drawing Survival rates. DDFS, LRFS and OS interrelated predictors were analyzed by Cox proportional-hazards regression with adjustment for age by univariate and multiple regression models. Multiple Cox proportional hazard (PH) models were used to obtain hazard ratios (HRs). The Fisher exact test and chi-square test were used to assess the relationship between related factors. All P values <0.05 were considered statistically significant.

 

4 Conclusions

In conclusion, our study demonstrated the higher expression of ER, PR and Ki67 post-NACT correlated with the higher biomarker expression pre-NACT in most patients, and more down-regulated than up-regulated expression after NACT. The change range of biomarker expression before and after chemotherapy were all mainly between 10 to 30 which we considered as medium changes. Higher difference PR expression change after NACT indicated worse distant disease-free survival and overall survival, while the patients with PR high expression before NACT and changed to PR negative expression after NACT considered as worst prognosis. Furthermore, decreasing expression of Ki67 had fewer local recurrence compared with Ki67 increasing expression after NACT. These findings might help optimize the choice of targeted therapy and improve the predictive effect to patient survival. For further research, we should pay more attention to more signal transduction pathways and molecular mechanism to provide a new rationale for the prevention, diagnosis and treatment of breast cancer.

 

Authors’ contributions

DWZ designed this study. YZ collected data and performed the ultrasound and mammographic procedures. YZ wrote this manuscript. DWZ revised the manuscript. All authors read and approved the final manuscript.

 

Acknowledgments

This work was supported by the Heilongjiang scientific research project (grants 201810).

 

References

Amat S., Abrial C., Penault-Llorca F., Delva R., Bougnoux P., and Leduc B., 2005, High prognostic significance of residual disease after neoadjuvant chemotherapy: a retrospective study in 710 patients with operable breast cancer, Breast Cancer Res Treat, 94: 255-263

https://doi.org/10.1007/s10549-005-9008-8

PMid:16267618

 

Cance W.G., Carey L.A., and Calvo B.F., 2002, Long-term outcome of neoadjuvant therapy for locally advanced breast carcinoma: effective clinical downstaging allows breast preservation and predicts outstanding local control and survival, Ann Surg., 236(3): 295-302

https://doi.org/10.1097/00000658-200209000-00006

PMid:12192316 PMCid:PMC1422583

 

Carey L.A., Dees E.C., Sawyer L., Gatti L., Moore D.T., Collichio F., Ollila D.W., Sartor C.I., Graham M.L., and Perou C.M., 2007, The triple negative paradox: primary tumor chemosensitivity of breast cancer subtypes, Clin Cancer Res., 13: 2329-2334

https://doi.org/10.1158/1078-0432.CCR-06-1109

PMid:17438091

 

Cheang M.C., Voduc D., Bajdik C., Leung S., McKinney S., Chia S.K., Perou C.M., and Nielsen T.O., 2008, Basal-like breast cancer defined by five biomarkers has superior prognostic value than triple-negative phenotype, Clin Cancer Res., 14: 1368-376

https://doi.org/10.1158/1078-0432.CCR-07-1658

PMid:18316557

 

Cheang M.C., Chia S.K., Voduc D., Gao D., Leung S., Snider J., Watson M., Davies S., Bernard P.S., and Parker J.S., 2009, Ki67 index, HER2 status, and prognosis of patients with luminal B breast cancer, J Natl Cancer Inst., 101: 736-750

https://doi.org/10.1093/jnci/djp082

PMid:19436038 PMCid:PMC2684553

 

Chen S., Chen C.M., Yu K.D., Zhou R.J., and Shao Z.M., 2012, Prognostic value of a positive-to-negative change in hormone recep-tor status after neoadjuvant chemotherapy in patients with hormone receptor-positive breast cancer, Ann Surg Oncol., 19: 3002-3011

https://doi.org/10.1245/s10434-012-2318-2

PMid:22437200

 

Colleoni M., and Goldhirsch A., 2014, Neoadjuvant chemotherapy for breast cancer: any progress? Lancet Oncol., 15: 131-132

https://doi.org/10.1016/S1470-2045(13)70584-9

 

Davies C., Godwin J., Gray R., Clarke M., Cutter D., and Darby S., 2011, Relevance of breast cancer hormone receptors and other factors to the efficacy of adjuvant tamoxifen: patient-level meta-analysis of randomised trials, Lancet, 378: 771-84

https://doi.org/10.1016/S0140-6736(11)60993-8

 

Davis D.W., Buchholz T.A., Hess K.R., Sahin A.A., Valero V., and McConkey D.J., 2003, Automated quantification of apoptosis after neoadjuvant chemotherapy for breast cancer: early assessment predicts clinical response., Clin Cancer Res., 9: 955-960

PMid:12631592

 

Edge S.B., Byrd S.B., Compton C.C., Fritz A.G., Greene F.L., and Trotti A., editors, 2010, American Joint Committee on Cancer (AJCC) cancer staging manual, 7th ed. New York, NY, USA: Springer

 

Feeley L.P., Mulligan A.M., Pinnaduwage D., Bull S.B., and Andrulis I.L., 2014, Distinguishing luminal breast cancer subtypes by Ki67, progesterone receptor or TP53 status provides prognostic information, Modern pathology: an official journal of the United States and Canadian Academy of Pathology, Inc., 27: 554-561

https://doi.org/10.1038/modpathol.2013.153

PMid:24051696

 

Ganem G., Tubiana-Hulin M., Fumoleau P., et al., 2003, Phase II trial combining docetaxel and doxorubicin as neoadjuvant chemotherapy in patients with operable breast cancer, Ann Oncol., 14: 1623-1628

https://doi.org/10.1093/annonc/mdg449

PMid:14581269

 

Goldhirsch A., Winer E.P., Coates A.S., Gelber R.D., Piccart-Gebhart M., Thurlimann B., Senn H.J., and Panel M., 2013, Personalizing the treatment of women with early breast cancer: highlights of the St Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2013, Ann Oncol., 24: 2206-2223

https://doi.org/10.1093/annonc/mdt303

PMid:23917950 PMCid:PMC3755334

 

Hugh J., Hanson J., Cheang M.C., Nielsen T.O., Perou C.M., Dumontet C., Reed J., Krajewska M., Treilleux I., Rupin M., Magherini E., Mackey J., Martin M., and Vogel C., 2009, Breast cancer subtypes and response to docetaxel in nodepositive breast cancer: use of an immunohistochemical definition in the BCIRG 001 trial, J Clin Oncol., 27: 1168-1176

https://doi.org/10.1200/JCO.2008.18.1024

PMid:19204205 PMCid:PMC2667821

 

Jin X., Jiang Y.Z., Chen S., Yu K.D., Shao Z.M., and Di G.H., 2015, Prognostic value of receptor conversion after neoadjuvant chemotherapy in breast cancer patients: a prospective observational study, Oncotarget, 6(11): 9600-9611

https://doi.org/10.18632/oncotarget.3292

PMid:25826079 PMCid:PMC4496242

 

Ko E.S., Han B.K., Kim R.B., Ko E.Y., Shin J.H., Hahn S.Y., Nam S.J., Lee J.E., Lee S.K., Im Y.H., and Park Y.H., 2013, Analysis of factors that influence the accuracy of magnetic resonance imaging for predicting response after neoadjuvant chemotherapy in locally advanced breast cancer, Ann Surg Oncol., 20: 2562-2568

https://doi.org/10.1245/s10434-013-2925-6

PMid:23463090

 

Kurosumi M., 2004, Significance of histopathological evaluation in primary therapy for breast cancer-recent trends in primary modality with pathological complete response (pCR) as endpoint, Breast Cancer, 11: 139-147

https://doi.org/10.1007/BF02968293

 

Ogston K.N., Miller I.D., and Payne S., 2003, A new histological grading system to assess response of breast cancers to primary chemotherapy: prognostic significance and survival, Breast, 12(5): 320-327

https://doi.org/10.1016/S0960-9776(03)00106-1

 

Parkin D.M., Bray F., Ferlay J., and Pisani P., 2005, Global cancer statistics, 2002, CA Cancer J Clin., 55: 74-108

https://doi.org/10.3322/canjclin.55.2.74

PMid:15761078

 

Parker J.S., Mullins M., Cheang M.C., et al., 2009, Supervised risk predictor of breast cancer based on intrinsic subtypes, J Clin Oncol., 27(8): 1160-1167

https://doi.org/10.1200/JCO.2008.18.1370

PMid:19204204 PMCid:PMC2667820

 

Rakha E.A., El-Sayed M.E., Green A.R., Paish E.C., Powe D.G., and Gee J., 2007, Biologic and clinical characteristics of breast cancer with single hormone receptor positive phenotype, J Clin Oncol., 25: 4772-4778

https://doi.org/10.1200/JCO.2007.12.2747

PMid:17876012

 

Rastogi P., Anderson S.J., Bear H.D., Geyer C.E., Kahlenberg M.S., Robidoux A., and Margolese R.G., 2008, Preoperative chemotherapy: updates of National Surgical Adjuvant Breast and Bowel Project Pro-tocols B-18 and B-27, J Clin Oncol., 26: 778-785

https://doi.org/10.1200/JCO.2007.15.0235

PMid:18258986

 

Read R.L., Flitcroft K., Snook K.L., Boyle F.M., and Spillane A.J., 2015, Utility of neoadjuvant chemotherapy in the treatment of operable breast cancer, ANZ J Surg., 85: 315-320

https://doi.org/10.1111/ans.12975

PMid:25612239

 

Sauter G., Lee J., Bartlett J.M., Slamon D.J., and Press M.F., 2009, Guidelines for human epidermal growth factor receptor 2 testing: biologic and methodologic considerations, J Clin Oncol., 27: 1323-1333

https://doi.org/10.1200/JCO.2007.14.8197

PMid:19204209

 

Shet T., Agrawal A., Chinoy R., Havaldar R., Parmar V., and Badwe R., 2007, Changes in the tumor grade and biological markers in locally advanced breast cancer after chemotherapy implications for a pathologist, Breast J., 13: 457-464

https://doi.org/10.1111/j.1524-4741.2007.00465.x

PMid:17760666

 

Stearns V., Singh B., Tsangaris T., et al., 2003, A prospective randomized pilot study to evaluate predictors of response in serial core biopsies to single agent neoadjuvant doxorubicin or paclitaxel for patients with locally advanced breast cancer, Clin Cancer Res., 9: 124-133

PMid:12538460

 

Sueta A., Yamamoto Y., Hayashi M., Yamamoto S., Inao T., Ibusuki M., Murakami K., and Iwase H., 2014, Clinical significance of pretherapeutic Ki67 as a predictive parameter for response to neoad-juvant chemotherapy in breast cancer; is it equally useful across tumor subtypes? Surgery, 155: 927-935

https://doi.org/10.1016/j.surg.2014.01.009

PMid:24582496

 

Thompson A.M., and Moulder-Thompson S.L., 2012, Neoadjuvant treat-ment of breast cancer, Ann Oncol., 10: x231-236

https://doi.org/10.1093/annonc/mds324

PMid:22987968 PMCid:PMC6278992

 

Tordai A., Wang J., Andre F., Liedtke C., Yan K., Sotiriou C., Hortobagyi G.N., Symmans W.F., and Pusztai L., 2008, Evaluation of biological pathways involved in chemotherapy response in breast cancer, Breast Cancer Res., 10(2): R37

https://doi.org/10.1186/bcr2088

PMid:18445275 PMCid:PMC2397539

 

van de Ven S., Smit V.T., Dekker T.J., Nortier J.W., and Kroep J.R., 2011, Discordances in ER, PR and HER2 receptors after neoadjuvant chemotherapy in breast cancer, Cancer Treat Rev., 37: 422-430

PMid:21177040

 

Wolff A.C., 2000, Davidson NE. Primary systemic therapy in operable breast cancer, J Clin Oncol., 18: 1558-1569

https://doi.org/10.1200/JCO.2000.18.7.1558

PMid:10735905

 

Zhao Y., Dong X.Q., Li R.G., Ma X., Song J., Li Y.J., and Zhang D.W., 2015, Evaluation of the pathological response and prognosis following neoadjuvant chemotherapy in molecular subtypes of breast cancer, Onco Targets Ther., 8: 1511-1521

PMid:26150728 PMCid:PMC4480585

Cancer Genetics and Epigenetics
• Volume 6
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