Literature DB >> 31422497

Ki-67 (30-9) scoring and differentiation of Luminal A- and Luminal B-like breast cancer subtypes.

Giuseppe Viale1,2, Amy E Hanlon Newell3, Espen Walker3, Greg Harlow4, Isaac Bai3, Leila Russo1, Patrizia Dell'Orto1, Patrick Maisonneuve5.   

Abstract

INTRODUCTION: Ki-67 labeling index assessed by immunohistochemical assays has been shown useful in assessing the risk of recurrence for estrogen receptor (ER)-positive HER2-negative breast cancers (BC) and distinguishing Luminal A-like from Luminal B-like tumors. We aimed to assess the performance of the Ventana CONFIRM anti-Ki-67 (30-9) Rabbit Monoclonal Primary Antibody.
METHODS: We constructed a case-cohort design based on a random sample (n = 679) of all patients operated on for a first primary, non-metastatic, ER-positive, HER2-negative BC at the European Institute of Oncology (IEO) Milan, Italy during 1998-2002 and all additional patients (n = 303) operated during the same period, who developed an event (metastasis in distant organs or death due to BC as primary event) and were not included in the previous subset. Multivariable Cox proportional hazards regression with inverse subcohort sampling probability weighting was used to evaluate the risk of event according to Ki-67 (30-9) and derived intrinsic molecular subtype, using previously defined cutoff values, i.e., respectively 14% and 20%.
RESULTS: Ki-67 was < 14% in 318 patients (32.4%), comprised between 14 and 19% in 245 patients (24.9%) and ≥ 20 in 419 patients (42.7%). At multivariable analysis, the risk of developing distant disease was 1.88 (95% CI 1.20-2.93; P = 0.006) for those with Ki-67 comprised between 14 and 19%, and 3.06 (95% CI 1.93-4.84; P < 0.0001) for those with Ki-67 ≥ 20% compared to those with Ki-67 < 14%. Patients with Luminal B-like BC had an approximate twofold risk of developing distant disease (HR = 1.91; 95% CI 1.35-2.71; P = 0.0003) than patients with Luminal A-like BC defined using Ki-67 (30-9).
CONCLUSIONS: Ki-67 evaluation using the 30-9 rabbit monoclonal primary antibody was able to stratify patients with ER-positive HER2-negative BC into prognostically distinct groups. Ki-67 assessment, with strict adherence to the international recommendations, should be included among the clinically useful biological parameters for the best treatment of patients with BC.

Entities:  

Keywords:  Biomarker; Differentiation; Ki-67; Luminal A; Luminal B

Mesh:

Substances:

Year:  2019        PMID: 31422497      PMCID: PMC6797656          DOI: 10.1007/s10549-019-05402-w

Source DB:  PubMed          Journal:  Breast Cancer Res Treat        ISSN: 0167-6806            Impact factor:   4.872


Introduction

Ki-67 is a nuclear antigen expressed by all proliferating cells during late G1 through the M phases of the cell cycle, peaking in the G2-M and with a rapid decline after mitosis [1]. Ki-67 labeling index assessed by immunohistochemical assays is a powerful prognostic marker in breast cancer. It is especially useful in assessing the risk of recurrence for estrogen receptor (ER)-positive HER2-negative breast cancers, where it may be considered a surrogate of the molecular assays for distinguishing Luminal A-like from Luminal B-like tumors [2]. Despite methodological problems still exist in the determination of Ki-67 in the routine clinical practice, both the Panellists of the St. Gallen Consensus [3] and the European Group on Tumor Markers (EGTM) [4] have endorsed use of Ki-67 in combination with established prognostic factors for determining prognosis, especially if values are low (e.g. < 10% of immunostained tumor cells) or high (e.g. > 25% cell staining). The higher cutoff value is based on a meta-analysis showing that a threshold of > 25% cell staining was associated with a greater risk of death compared with lower values [5]. The mouse monoclonal antibody MIB-1 was the first antibody to be raised against a formalin-resistant epitope of Ki-67, and it has been extensively used in both clinical research and routine practice [6]. We evaluated the performance of the CONFIRM anti-Ki-67 (30-9) Rabbit Monoclonal Primary Antibody in assessing the risk of distant relapses in a large series of patients with ER-positive HER2-negative breast cancer treated and followed up in a single Institution.

Materials and methods

Patients selection

The initial cohort (9415 patients) comprised all women operated on for a first primary, non-metastatic, ER-positive HER2 negative, breast cancer (BC) at the European Institute of Oncology (IEO) Milan, Italy, who did not receive neoadjuvant treatment [7]. We subsequently restricted the cohort to 3986 patients operated on between 1998 and 2002 and for whom long-term follow-up data were available. A case–cohort [8] was built by randomly selecting approximately 17% of the above cohort (n = 679). Additional patients (n = 303) who developed an event (metastasis in distant organs or death due to BC as primary events) were added to this cohort (Supplementary Fig. 1).

Laboratory methods

Ki-67 was evaluated using the VENTANA CONFIRM anti-Ki-67 (30-9) Rabbit Monoclonal Primary Antibody (Ventana Medical Systems, Inc., Tucson, AZ) using OptiView IHC DAB detection on the BenchMark ULTRA advanced staining platform. The stained slides were evaluated at the IEO by certified pathologists trained to score Ki-67 according to the recommendations of the International Ki-67 in Breast Cancer Working Group [9]. Samples were retrieved from the Pathology archives with Institutional Review Board approval and classified as ‘Luminal A-like’; estrogen receptor (ER)-positive, HER2-negative tumors with “low” Ki-67 (< 14%) or with “intermediate” Ki-67 (14–19%) and “high” progesterone receptor expression (PgR ≥ 20%), and ‘Luminal B-like’; HER2 negative, tumors ER positive, HER2 negative, with “intermediate” Ki-67 (14–19%) and “low” PgR (< 20%) or with “high” Ki-67 (≥ 20%) [7] (Supplementary Table 1).

Statistical methods

Associations between clinicopathological characteristics and Ki-67 expression were evaluated with the Mantel–Haenszel test for trend. The main outcome was distant disease-free survival (DDFS) and was calculated from the date of surgery to the date of any first event or the date of last contact with the patient. Distant disease was defined as the occurrence of metastasis in distant organs or death due to BC as primary events. The rate of events in the subcohort was calculated dividing the number of events recorded during follow-up by the total number of patient-years accumulated during the observation period and 95% confidence intervals calculated using the mid-P exact method. Cumulative incidence curves were drawn for patients in the subcohort using the Kaplan–Meier method and difference between patient subgroups was assessed using the log-rank test. Multivariable Cox proportional hazards regression with inverse subcohort sampling probability weighting was used to evaluate the risk of metastasis or death from BC across groups in the combined case–cohort [10, 11]. In the multivariable analysis, Ki-67 was considered either as a continuous variable, expressing the hazard ratio (HR) for each 10% increase of Ki-67 labeling index or was categorized using the same cutoff values used for the definition of surrogate BC molecular subtypes, i.e., respectively 14% and 20% [7]. Other variables considered in the multivariable analysis include menopausal status, pathological T (pT1, pT2, pT3/4), regional lymph node status (pN0, pN+), tumor grade (G1, G2, G3), peritumoral vascular invasion (PVI) (absent, present), estrogen receptor (ER) and progesterone receptor (PgR) expression (< 20% vs. ≥ 20%) and adjuvant treatment (none, hormone therapy alone, or hormone therapy plus chemotherapy). Finally, restricted cubic spline Cox regression models were applied to assess dose–response relationships. Analyses were carried out with the SAS software (version 9.4, Cary NC). P values were two sided. P < 0.05 were considered statistically significant.

Results

The case–cohort comprised 982 patients (679 patients are part of the 1998–2002 subcohort (including 84 with event) and 303 are patients who developed event outside of the subcohort (Supplementary Fig. 1). Of the 387 events, 10 were death from breast cancer as a first event, and the remaining 377 were distant metastases, with a prevalence of bone metastases (155 events) followed by lung (46 events) and liver metastases (44 events). Ninety-five patients developed multiple metastases as first event. Distribution of Ki-67 according to clinicopathological characteristics is displayed in Table 1. Overall, the median Ki-67 expression was 18%, in 318 patients (32.4%) it was < 14%, in 245 patients (24.9%) it was comprised between 14 and 19% and in 419 patients (42.7%) it was ≥ 20%. Distribution was significantly different across all subgroups evaluated, Ki-67 expression being significantly higher in premenopausal women P = 0.0002), and being directly associated with pT, pN, tumor grade, presence of PVI, and inversely associated with the expression of ER and PgR receptors (P < 0.0001 for all the associations). Particularly elevated median Ki-67 expression (27%) was found in patients with poorly differentiated tumors and in patients with tumors showing low (1–19% immunoreactive tumor cells) or moderate (20–49% immunoreactive tumor cells) ER expression, and with a median Ki-67 value of 35% and 27%, respectively. Lowest median Ki-67 was observed in patients with well-differentiated tumors (median 9%).
Table 1

Distribution of Ki-67 (30-9) according to selected clinicopathological characteristics

PatientsKi-67 (30-9)
Median (25th–75th)Mean Std. Dev< 14%N (%)14–19%N (%)≥ 20%N (%)P value*
All98218 (12–25)19.6 ± 11.9318 (100)245 (100)419 (100)
Menopausal status
 Pre/peri43419 (13–26)21.2 ± 12.6115 (36.2)109 (44.5)210 (50.1)
 Post54816 (11–24)18.4 ± 11.1203 (63.8)136 (55.5)209 (49.9)0.0002
pT
 pT159015 (10–22)16.9 ± 10.2245 (77.0)160 (65.3)185 (44.2)
 pT234322 (16–29)24.3 ± 13.461 (19.2)72 (29.4)210 (50.1)
 pT3/44919 (14–25)19.7 ± 8.512 (3.8)13 (5.3)24 (5.7)< 0.0001
pN
 pN045916 (10–22)17.7 ± 12.5195 (61.3)107 (43.7)157 (37.5)
 1–3 Positive nodes29218 (13–25)20.0 ± 11.281 (25.5)86 (35.1)125 (29.8)
 ≥ 4 Positive nodes21222 (16–29)23.3 ± 10.235 (11.0)48 (19.6)129 (30.8)< 0.0001
 pNx1916 (7–25)19.5 ± 14.57 (2.2)4 (1.6)8 (1.9)
Grade
 G11819 (5–14)10.5 ± 6.6131 (41.2)34 (13.9)16 (3.8)
 G248916 (12–21)17.0 ± 8.0169 (53.1)170 (69.4)150 (35.8)
 G328627 (22–34)30.1 ± 13.08 (2.5)35 (14.3)243 (58.0)< 0.0001
 Unknown2617 (10–24)17.3 ± 7.810 (3.1)6 (2.4)10 (2.4)
PVI
 Absent67216 (10–23)17.8 ± 11.3263 (82.7)169 (69.0)240 (57.3)
 Present31022 (16–28)23.5 ± 12.255 (17.3)76 (31.0)179 (42.7)< 0.0001
ER
 1–19%1735 (28–50)38.7 ± 19.50 (0.0)4 (1.6)13 (3.1)
 20–49%6127 (17–30)25.2 ± 13.013 (4.1)12 (4.9)36 (8.6)
 ≥ 50%90419 (12–27)21.6 ± 13.5305 (95.9)229 (93.5)370 (88.3)< 0.0001
PgR
 1–20%30921 (14–30)23.2 ± 14.286 (27.0)75 (30.6)148 (35.3)
 20–49%18022 (14–29)22.9 ± 13.352 (16.4)38 (15.5)90 (21.5)
≥ 50%49318 (12–26)21.2 ± 13.6180 (56.6)132 (53.9)181 (43.2)0.001
Adjuvant treatment
 None4112 (6–19)15.0 ± 13.325 (7.9)6 (2.4)10 (2.4)
 Hormone therapy47014 (10–21)15.8 ± 9.4217 (68.2)119 (48.6)134 (32.1)
 Chemotherapy47022 (16–29)23.9 ± 12.576 (23.9)120 (49.0)274 (65.4)< 0.0001

*P-value based on the Mantel–Haenszel Chi square test for trend

Distribution of Ki-67 (30-9) according to selected clinicopathological characteristics *P-value based on the Mantel–Haenszel Chi square test for trend In the subcohort, 84 patients developed distant disease or died from BC as first event during follow-up, corresponding to an event rate of 1.60 per 100 patient-year (Table 2). The event rate increased from 0.61 per 100 patient-year for patients with low Ki-67 (< 14%), to 1.47 per 100 patient-year for those with intermediate Ki-67 (14–19%) and to 3.12 per 100 patient-year for those with Ki-67 ≥ 20%. The 10-year cumulative incidence of distant metastasis (or BC-related death as first event) according to categories of Ki-67 is shown in Fig. 1. The event rate was about constant over time in the three groups. At 5 years and 10 years, respectively, 2.7% (95% CI 1.3–5.5) and 6.4% (95% CI 3.8–10.7) of patients with Ki-67 < 14% developed an event against 6.8% (95% CI 3.8–11.9) and 13.5% (95% CI 8.9–20.3) of those with intermediate Ki-67, and 15.2% (95% CI 11.0–20.8) and 26.6% (95% CI 20.7–33.8) of those with high Ki-67.
Table 2

Distribution of events and multivariable analysis

Sub–cohort (n = 679)Additional cases (n = 303)Case–cohort (n = 982)
PatientsEventsEvent rate per 100 patient-year (95% CI)HR (95% CI)aP-value
Ki-67 (30-9)b
 < 14%278140.61 (0.35–1.00)401.00
 14–19%171201.47 (0.92–2.23)741.88 (1.20–2.93)0.006
 ≥ 20%230503.12 (2.34–4.08)1893.06 (1.93–4.84)< 0.0001
Menopausal status
 Pre/peri289341.50 (1.05–2.07)1451.00
 Post390501.68 (1.26–2.20)1581.43 (0.82–2.49)0.20
pT
 pT1466340.88 (0.62–1.22)1241.00
 pT2191443.51 (2.58–4.67)1521.75 (1.24–2.45)0.001
 pT3/42264.41 (1.79–9.18)273.26 (1.66–6.39)0.0006
pN
 pN0378180.57 (0.35–0.89)811.00
 pN+402643.22 (2.50–4.09)1211.86 (1.25–2.78)0.002
Grade
 G116660.42 (0.17–0.87)151.00
 G2343361.36 (0.96–1.86)1461.90 (1.06–3.39)0.03
 G3151403.91 (2.83–5.28)1352.43 (1.27–4.65)0.008
PVI
 Absent510451.10 (0.81–1.46)1621.00
 Present169393.36 (2.42–4.55)1411.53 (1.11–2.12)0.009
ER
 ≥ 20%665811.57 (1.26–1.94)3001.00
 < 20%1433.06 (0.78–8.33)31.05 (0.43–2.61)0.91
PgR
 ≥ 20%474531.45 (1.10–1.88)1991.00
 < 20%205311.95 (1.35–2.73)1041.01 (0.72–1.41)0.98
Adjuvant treatment
 None3431.22 (0.31–3.33)71.50 (0.61–3.69)0.37
 Hormone therapy382200.64 (0.39–0.94)881.00
 Chemotherapy262613.18 (2.45–4.06)2081.73 (1.11–2.69)0.02

aHazards ratio (HR and 95% confidence intervals (CI) obtained from Cox proportional Hazards regression with inverse subcohort sampling probability weighting as defined in Miettinen (1976) using the SAS macro CCREGRESSION provided by Kulathinal et al. (2007)

bResults of an alternative multivariable model with Ki-67 (30-9) set as a continuous variable shows HR = 1.25 (95% CI 1.08–1.44; P = 0.002) for a 10% increase Ki-67, adjusted for menopausal status, pT, pN, grade, PVI, ER, PgR and adjuvant therapy

Fig. 1

Cumulative incidence of events in the subcohort (N = 679) and corresponding Hazards Ratios in the case–cohort (N = 982) according to Ki-67 (30-9) and derived intrinsic molecular subtype

Distribution of events and multivariable analysis aHazards ratio (HR and 95% confidence intervals (CI) obtained from Cox proportional Hazards regression with inverse subcohort sampling probability weighting as defined in Miettinen (1976) using the SAS macro CCREGRESSION provided by Kulathinal et al. (2007) bResults of an alternative multivariable model with Ki-67 (30-9) set as a continuous variable shows HR = 1.25 (95% CI 1.08–1.44; P = 0.002) for a 10% increase Ki-67, adjusted for menopausal status, pT, pN, grade, PVI, ER, PgR and adjuvant therapy Cumulative incidence of events in the subcohort (N = 679) and corresponding Hazards Ratios in the case–cohort (N = 982) according to Ki-67 (30-9) and derived intrinsic molecular subtype Dose–response in the subcohort was further evaluated in a plot based on a restricted cubic spline Cox regression model (Fig. 2): the rate of events increased linearly with the increasing expression of Ki-67 for values comprised between 0 and 30%. Above this threshold, the rate of events increased only slightly, and was based on a small fraction of the patients (only 79 (12%) patients in the subcohort had Ki-67 ≥ 30%).
Fig. 2

Event rate* in the subcohort and Hazard Ratio** for distant disease-free survival according to Ki-67 (HR set to 1.00 for Ki-67 = 14%) in the case–cohort

Event rate* in the subcohort and Hazard Ratio** for distant disease-free survival according to Ki-67 (HR set to 1.00 for Ki-67 = 14%) in the case–cohort In the case–cohort, including additional cases reported outside of the subcohort and after adjusting for other prognostic factors (menopausal status, pT, pN, grade, PVI, ER, and PgR) and for the type of adjuvant treatment received, Ki-67 remained significantly associated with DDFS. The relative risk of developing distant disease was 1.88 (95% CI 1.20–2.93; P = 0.006) for those with Ki-67 comprised between 14 and 19%, and 3.06 (95% CI 1.93–4.84; P < 0.0001) for those with Ki-67 ≥ 20% compared to those with Ki-67 < 14%. Other independent prognostic factors include pT, pN, tumor grade, PVI. Patients receiving chemotherapy were also at higher risk of events (Table 2). We used previously published criteria for the definition of surrogate BC molecular subtype using Ki-67 (Supplementary Table 1). In the subcohort, 400 (58.9%) patients were classified as having “luminal A-like” and 279 (41.1%) “luminal B-like” BC. The 5-year and 10-year cumulative incidence of distant metastasis (or BC-related death as first event) were respectively 4.2% (95% CI 2.6–6.8) and 8.2% (95% CI 5.7–11.9) in the Luminal A group and 13.2% (95% CI 9.6–17.9) and 24.5% (95% CI 19.4–30.8%) in the Luminal B group (log-rank P < 0.0001) (Fig. 1). In the whole case–cohort, multivariable analysis confirmed statistically significant increased risk of events for women with “Luminal B-Like” BC compared to women with “Luminal A-Like“ BC (HR = 1.91; 95% CI 1.35–2.71; P = 0.0003), after adjustment for menopausal status, pT, pN, grade, PVI and adjuvant therapy (Table 3).
Table 3

Distribution of events and multivariable analysis

Sub–cohort (n = 679)Additional cases (n = 303)Case–cohort (n = 982)
PatientsEventsEvent rate per 100 patient-year (95% CI)HR (95% CI)aP-value
Molecular subtype
Luminal A-like400270.83 (0.56–1.20)881.00
Luminal B-like279572.84 (2.17–3.65)2151.91 (1.35–2.71)0.0003

aHazards ratio (HR and 95% confidence intervals (CI) obtained from multivariable Cox proportional Hazards regression with inverse subcohort sampling probability weighting as defined in Miettinen (1976) using the SAS macro CCREGRESSION provided by Kulathinal et al. (2007), adjusted for menopausal status, pT, pN, grade, PVI and adjuvant therapy

Distribution of events and multivariable analysis aHazards ratio (HR and 95% confidence intervals (CI) obtained from multivariable Cox proportional Hazards regression with inverse subcohort sampling probability weighting as defined in Miettinen (1976) using the SAS macro CCREGRESSION provided by Kulathinal et al. (2007), adjusted for menopausal status, pT, pN, grade, PVI and adjuvant therapy

Discussion

Ki-67 labeling index is a clinically validated prognostic factor in early breast cancer. In the neoadjuvant setting, it predicts the likelihood of pathological complete response (pCR) to chemotherapy. Furthermore, Ki-67 in the residual tumor [12, 13], and changes of Ki-67 labeling index between primary and residual tumors are prognostic for long-term outcome [14, 15]. Decline of Ki-67 after few weeks of neoadjuvant endocrine therapy is correlated with a better long-term outcome of ER-positive HER2-negative disease [16] and Ki67 assessment in the residual tumor after neoadjuvant endocrine treatment is predictive of long-term outcome [17]. In the adjuvant setting, Ki-67 is a prognostic marker for disease-free and overall survival independent of tumor stage [18, 19]. Despite its undisputed prognostic value, however, Ki-67 labeling index per se is not predictive of the benefit of adding chemotherapy to endocrine therapy in the treatment of patients with ER-positive HER2-negative early breast cancer [20]. To inform the choice of systemic treatment for these patients, Ki-67 labeling index should be used in combination with other parameters, including tumor grade and a quantitative evaluation of ER and progesterone receptor (PgR) expression [3]. This has also been endorsed by the updated guidelines from the European Group of Tumor Markers [4]. By using a similar multifactorial approach, we have previously proposed a surrogate immunohistochemical definition of Luminal A-like and Luminal B-like breast cancer [7] that could be helpful in tailoring the systemic treatment, especially when multiparameter molecular assays are not available. Most of the aforementioned studies have been conducted using the MIB-1 monoclonal antibody to Ki-67. Here, we have shown that Ki-67 evaluation using the Ventana 30-9 rabbit monoclonal primary antibody, was similarly able to stratify patients with ER-positive HER2-negative breast cancer into prognostically distinct groups. Ki-67 evaluation in this cohort enabled maximizing the number of patients classified as having ‘Luminal A-like’ intrinsic subtype for whom the use of cytotoxic drugs could be at large avoided. Indeed, 400 (58.9%) patients in the subcohort were classified as having “Luminal A-like” and 279 (41.1%) “Luminal B-like” BC. These figures are strikingly similar to those obtained by Cheang and colleagues [21] using a rabbit monoclonal antibody to Ki-67 (clone SP6) in a series of 2847 hormone receptor-positive breast carcinomas, and showing a 59% prevalence of Luminal A-like tumors. More recently, a study evaluating Ki-67 with the MIB-1 antibody in a series of 4718 patients with hormone receptor-positive disease also found a prevalence of Luminal A-like tumors of 58.2% [22]. Currently, the scientific community is still concerned about a perceived lack of accuracy and reproducibility in the assessment of Ki-67 in the clinical setting. Major steps toward a harmonization of Ki-67 scoring in breast cancer, however, have been already made [9, 23–25], and it may be predicted that Ki-67 assessment, with strict adherence to the international recommendations, will ultimately be included among the clinically useful biological parameters for the best treatment of patients with breast carcinoma. Below is the link to the electronic supplementary material. Supplementary material 1 (DOCX 47 kb)
  24 in total

1.  Changes in PgR and Ki-67 in residual tumour and outcome of breast cancer patients treated with neoadjuvant chemotherapy.

Authors:  E Montagna; V Bagnardi; G Viale; N Rotmensz; A Sporchia; G Cancello; A Balduzzi; V Galimberti; P Veronesi; A Luini; M G Mastropasqua; C Casadio; C Sangalli; A Goldhirsch; M Colleoni
Journal:  Ann Oncol       Date:  2014-11-19       Impact factor: 32.976

2.  Predictive value of tumor Ki-67 expression in two randomized trials of adjuvant chemoendocrine therapy for node-negative breast cancer.

Authors:  Giuseppe Viale; Meredith M Regan; Mauro G Mastropasqua; Fausto Maffini; Eugenio Maiorano; Marco Colleoni; Karen N Price; Rastko Golouh; Tiziana Perin; R W Brown; Anikó Kovács; Komala Pillay; Christian Ohlschlegel; Barry A Gusterson; Monica Castiglione-Gertsch; Richard D Gelber; Aron Goldhirsch; Alan S Coates
Journal:  J Natl Cancer Inst       Date:  2008-01-29       Impact factor: 13.506

3.  Case-cohort design in practice - experiences from the MORGAM Project.

Authors:  Sangita Kulathinal; Juha Karvanen; Olli Saarela; Kari Kuulasmaa
Journal:  Epidemiol Perspect Innov       Date:  2007-12-04

4.  Assessment of Ki67 in breast cancer: recommendations from the International Ki67 in Breast Cancer working group.

Authors:  Mitch Dowsett; Torsten O Nielsen; Roger A'Hern; John Bartlett; R Charles Coombes; Jack Cuzick; Matthew Ellis; N Lynn Henry; Judith C Hugh; Tracy Lively; Lisa McShane; Soon Paik; Frederique Penault-Llorca; Ljudmila Prudkin; Meredith Regan; Janine Salter; Christos Sotiriou; Ian E Smith; Giuseppe Viale; Jo Anne Zujewski; Daniel F Hayes
Journal:  J Natl Cancer Inst       Date:  2011-09-29       Impact factor: 13.506

5.  Proposed new clinicopathological surrogate definitions of luminal A and luminal B (HER2-negative) intrinsic breast cancer subtypes.

Authors:  Patrick Maisonneuve; Davide Disalvatore; Nicole Rotmensz; Giuseppe Curigliano; Marco Colleoni; Silvia Dellapasqua; Giancarlo Pruneri; Mauro G Mastropasqua; Alberto Luini; Fabio Bassi; Gianmatteo Pagani; Giuseppe Viale; Aron Goldhirsch
Journal:  Breast Cancer Res       Date:  2014-06-20       Impact factor: 6.466

6.  Prognostic value of automated KI67 scoring in breast cancer: a centralised evaluation of 8088 patients from 10 study groups.

Authors:  Mustapha Abubakar; Nick Orr; Frances Daley; Penny Coulson; H Raza Ali; Fiona Blows; Javier Benitez; Roger Milne; Herman Brenner; Christa Stegmaier; Arto Mannermaa; Jenny Chang-Claude; Anja Rudolph; Peter Sinn; Fergus J Couch; Peter Devilee; Rob A E M Tollenaar; Caroline Seynaeve; Jonine Figueroa; Mark E Sherman; Jolanta Lissowska; Stephen Hewitt; Diana Eccles; Maartje J Hooning; Antoinette Hollestelle; John W M Martens; Carolien H M van Deurzen; Manjeet K Bolla; Qin Wang; Michael Jones; Minouk Schoemaker; Jelle Wesseling; Flora E van Leeuwen; Laura Van 't Veer; Douglas Easton; Anthony J Swerdlow; Mitch Dowsett; Paul D Pharoah; Marjanka K Schmidt; Montserrat Garcia-Closas
Journal:  Breast Cancer Res       Date:  2016-10-18       Impact factor: 6.466

7.  Outcome prediction for estrogen receptor-positive breast cancer based on postneoadjuvant endocrine therapy tumor characteristics.

Authors:  Matthew J Ellis; Yu Tao; Jingqin Luo; Roger A'Hern; Dean B Evans; Ajay S Bhatnagar; Hilary A Chaudri Ross; Alexander von Kameke; William R Miller; Ian Smith; Wolfgang Eiermann; Mitch Dowsett
Journal:  J Natl Cancer Inst       Date:  2008-09-23       Impact factor: 13.506

8.  Ki67 index, HER2 status, and prognosis of patients with luminal B breast cancer.

Authors:  Maggie C U Cheang; Stephen K Chia; David Voduc; Dongxia Gao; Samuel Leung; Jacqueline Snider; Mark Watson; Sherri Davies; Philip S Bernard; Joel S Parker; Charles M Perou; Matthew J Ellis; Torsten O Nielsen
Journal:  J Natl Cancer Inst       Date:  2009-05-12       Impact factor: 13.506

9.  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.

Authors:  A Goldhirsch; E P Winer; A S Coates; R D Gelber; M Piccart-Gebhart; B Thürlimann; H-J Senn
Journal:  Ann Oncol       Date:  2013-08-04       Impact factor: 32.976

10.  Analytical validation of a standardized scoring protocol for Ki67: phase 3 of an international multicenter collaboration.

Authors:  Samuel C Y Leung; Torsten O Nielsen; Lila Zabaglo; Indu Arun; Sunil S Badve; Anita L Bane; John M S Bartlett; Signe Borgquist; Martin C Chang; Andrew Dodson; Rebecca A Enos; Susan Fineberg; Cornelia M Focke; Dongxia Gao; Allen M Gown; Dorthe Grabau; Carolina Gutierrez; Judith C Hugh; Zuzana Kos; Anne-Vibeke Lænkholm; Ming-Gang Lin; Mauro G Mastropasqua; Takuya Moriya; Sharon Nofech-Mozes; C Kent Osborne; Frédérique M Penault-Llorca; Tammy Piper; Takashi Sakatani; Roberto Salgado; Jane Starczynski; Giuseppe Viale; Daniel F Hayes; Lisa M McShane; Mitch Dowsett
Journal:  NPJ Breast Cancer       Date:  2016-05-18
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  9 in total

1.  How to Perform Repeat Sentinel Node Biopsy Safely After a Previous Mastectomy: Technical Features and Oncologic Outcomes.

Authors:  Elisa Vicini; Maria Cristina Leonardi; Sabrina Kahler Ribeiro Fontana; Eleonora Pagan; Vincenzo Bagnardi; Laura Gilardi; Anna Cardillo; Paola Rafaniello Raviele; Manuela Sargenti; Consuelo Morigi; Mattia Intra; Paolo Veronesi; Viviana Galimberti
Journal:  Ann Surg Oncol       Date:  2021-11-08       Impact factor: 5.344

Review 2.  Breast cancer in the era of precision medicine.

Authors:  Negar Sarhangi; Shahrzad Hajjari; Seyede Fatemeh Heydari; Maryam Ganjizadeh; Fatemeh Rouhollah; Mandana Hasanzad
Journal:  Mol Biol Rep       Date:  2022-06-22       Impact factor: 2.742

3.  Automated quantitative analysis of Ki-67 staining and HE images recognition and registration based on whole tissue sections in breast carcinoma.

Authors:  Min Feng; Yang Deng; Libo Yang; Qiuyang Jing; Zhang Zhang; Lian Xu; Xiaoxia Wei; Yanyan Zhou; Diwei Wu; Fei Xiang; Yizhe Wang; Ji Bao; Hong Bu
Journal:  Diagn Pathol       Date:  2020-05-29       Impact factor: 2.644

4.  Protein co-expression networks identified from HOT lesions of ER+HER2-Ki-67high luminal breast carcinomas.

Authors:  Kimito Yamada; Toshihide Nishimura; Midori Wakiya; Eiichi Satoh; Tetsuya Fukuda; Keigo Amaya; Yasuhiko Bando; Hiroshi Hirano; Takashi Ishikawa
Journal:  Sci Rep       Date:  2021-01-18       Impact factor: 4.379

Review 5.  Genomic Assays in Node Positive Breast Cancer Patients: A Review.

Authors:  Maroun Bou Zerdan; Maryam Ibrahim; Clara El Nakib; Rayan Hajjar; Hazem I Assi
Journal:  Front Oncol       Date:  2021-02-16       Impact factor: 6.244

Review 6.  Tetramethylpyrazine: A Review of Its Antitumor Potential and Mechanisms.

Authors:  Shaojie Yang; Shuodong Wu; Wanlin Dai; Liwei Pang; Yaofeng Xie; Tengqi Ren; Xiaolin Zhang; Shiyuan Bi; Yuting Zheng; Jingnan Wang; Yang Sun; Zhuyuan Zheng; Jing Kong
Journal:  Front Pharmacol       Date:  2021-12-16       Impact factor: 5.810

7.  RNF6 promotes the migration and invasion of breast cancer by promoting the ubiquitination and degradation of MST1.

Authors:  Yajuan Huang; Yufeng Zou; Zhigang Jie
Journal:  Exp Ther Med       Date:  2021-12-06       Impact factor: 2.751

8.  FDG positron emission tomography imaging and ctDNA detection as an early dynamic biomarker of everolimus efficacy in advanced luminal breast cancer.

Authors:  Andrea Gombos; David Venet; Michail Ignatiadis; Géraldine Gebhart; Lieveke Ameye; Peter Vuylsteke; Patrick Neven; Vincent Richard; Francois P Duhoux; Jean-Francois Laes; Françoise Rothe; Christos Sotiriou; Marianne Paesmans; Ahmad Awada; Thomas Guiot; Patrick Flamen; Martine Piccart-Gebhart
Journal:  NPJ Breast Cancer       Date:  2021-09-21

9.  Accuracy and Limitations of Sentinel Lymph Node Biopsy after Neoadjuvant Chemotherapy in Breast Cancer Patients with Positive Nodes.

Authors:  Sofia Aragon-Sanchez; M Reyes Oliver-Perez; Ainhoa Madariaga; M Jose Tabuenca; Mario Martinez; Alberto Galindo; M Luisa Arroyo; Marta Gallego; Marta Blanco; Eva M Ciruelos-Gil
Journal:  Breast J       Date:  2022-08-05       Impact factor: 2.269

  9 in total

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