Predictive value of Ki67 for complete pathological response to
neoadjuvant chemotherapy in patients with breast cancer
Safaa M.M. Abd El Khaleka
, Mona Q.R. Mohammedb
, Fatma S.S. Hafeza
a
Department of Pathology, Ain Shams
University, Faculty of Medicine, Cairo, Egypt,
b
Department of Clinical Oncology, Ain Shams
University, Faculty of Medicine, Cairo, Egypt
Correspondence to Safaa M.M.A. El Khalek,
MD, Department of Pathology, Faculty of
Medicine, Ain Shams University, Ain Shams
University, Abbassyia Square, Ramsis Street,
11566 Cairo, Egypt. Tel: 01026440919;
fax: 2434601- 24346753; e-mails:
safaamahmoud@med.asu.edu.eg,
Dr.safaa.mahmoud@gmail.com
Received: 23 September 2021
Revised: 2 October 2021
Accepted: 14 October 2021
Published: 12 July 2022
Egyptian Journal of Pathology 2021,
41:194–203
Background
Neoadjuvant chemotherapy is an essential therapeutic approach for patients with
breast cancer, with the goal of improving pathological complete response rate
(pCR) by decreasing staging and evaluating treatment response for prognostic
purposes. Proliferation index estimated by Ki67 has a significant effect on tumor
prognosis with a cutoff value of 30%. However, data are still insufficient about the
predictive cutoff value for pCR after neoadjuvant chemotherapy. The objective of
this study was to evaluate the pathologic response after neoadjuvant chemotherapy
in patients with breast cancer, to examine the effect of Ki67 index on the rate of
pathologic response with the estimation of the proper predictive cutoff value. We
also studied the correlation of the pCR rate with different prognostic
histopathological parameters.
Patients and methods
The study included 84 patients with breast cancer who received neoadjuvant
chemotherapy. Baseline Ki67 immunohistochemical expression was evaluated.
Results
Overall, 25% of the patients achieved pCR. The optimal cutoff point for Ki67 was
25%. There is a significant correlation between pCR and tumor-infiltrating
lymphocytes (TILs), T stage before therapy, lymph node metastasis, and
postmenopausal state. Linear regression analysis showed that Ki67 and TILs
were associated with an increased rate of pCR after neoadjuvant therapy with a
highly significant correlation.
Conclusion
In patients with breast cancer, Ki67 expression with a cutoff threshold of 25% could
be used to predict the probability of achieving a complete response to neoadjuvant
therapy. TILs are strongly associated with pCR.
Keywords:
breast cancer, IHC, Ki67, neoadjuvant therapy, pCR, TILs
Egypt J Pathol 41:194–203
© 2021 Egyptian Journal of Pathology | Published by Wolters Kluwer - Medknow
1687-4277
Introduction
Breast cancer is the most frequent cancer diagnosed
globally in 2021, accounting for 30% of all female
cancers and the second cause of death after lung
cancer (Siegel et al., 2021). It accounts for 33% of
all female cancer cases in Egypt, with more than 22,000
new cases identified each year (Ibrahim et al., 2014).
Population growth, population pyramid change, and a
more westernized lifestyle are all likely to drive this
number enormously higher in coming years (Abdelaziz
et al., 2021).
In clinical practice, immunohistochemical
examinations of tumors based on the status of
hormone receptors (HRs) and human epidermal
growth factor 2 (HER2) are performed; this method
is simpler and less expensive and yields similar findings
for molecular subtypes (Goldhirsch et al., 2011; Kumar
et al., 2013).
Luminal A (HR+/HER2-/low Ki67), luminal B (HR
+/HER2-/+/high Ki67), HER2-enriched (HR-/
HER2+), and triple-negative breast cancers
(TNBCs) (HR-/HER2- ) are the molecular subtypes
of breast cancer (Hortobagyi et al., 2017). Each subtype
exhibits distinct prognosis and rates of recurrence and
needs different treatment strategies (Sørlie et al., 2001).
The mainstay for predicting tumor susceptibility to
hormone therapy and subsequent trastuzumab therapy
is now immunohistochemistry-based molecular
subtyping of breast cancer (Andre and Pusztai, 2006;
Goldhirsch et al., 2011).
Preoperative and postoperative chemotherapy
regimens are currently almost entirely defined by
clinical evidence and expert consensus depending on
recurrence risk factors and breast cancer subtype
(Coates et al., 2015). However, tumor tissues are
heterogeneous, and therefore the effects of a given
This is an open access journal, and articles are distributed under the terms
of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0
License, which allows others to remix, tweak, and build upon the work
non-commercially, as long as appropriate credit is given and the new
creations are licensed under the identical terms.
194 Original article
© 2021 Egyptian Journal of Pathology | Published by Wolters Kluwer - Medknow DOI: 10.4103/egjp.egjp_55_21
[Downloaded free from http://www.xep.eg.net on Wednesday, January 25, 2023, IP: 5.37.234.90]

chemotherapy treatment are not the same in every
patient. Individual differences in chemotherapy
response are not taken into account because standard
treatment guidelines are based on data from a large
sample size (Mukai et al., 2020).
Proliferation is the key driver of the prognostic
performance of the genomic tests that have been
developed to add prognostic information to
clinicopathological models and have a role in the
decision-making process (Wirapati et al., 2008).
Similarly, in addition to the traditional
histopathological variables, the assessment of
proliferation is one of the most important
parameters in making treatment decisions in patients
with breast cancer (Hayes, 2012).
Neoadjuvant chemotherapy is an essential therapeutic
approach for patients with breast cancer, with the goal
of improving pathological complete response (pCR)
rate by decreasing staging and evaluating treatment
response for prognostic purposes (Early Breast Cancer
Trialists’ Collaborative Group EBCTCG, 2018). In
patients with breast carcinoma, Ki67 LI
immunohistochemistry has recently been studied as a
potential predictive and prognostic biomarker in
attaining pCR after neoadjuvant chemotherapy (Lee
et al., 2013).
The International Ki67 in Breast Cancer Working
Group concluded that there is insufficient evidence
about Ki67 baseline prediction of chemotherapy
benefit (Nielsen et al., 2021).
Our study aimed to evaluate the cutoff predictive value
of baseline Ki67 for pCR after neoadjuvant
chemotherapy in patients with breast cancer and
correlate different prognostic clinicopathologic
parameters with the rate of pathologic response.
Material and method
Specimen collection
This cohort retrospective study was conducted on 84
cases of invasive mammary carcinoma that were
diagnosed on core biopsy specimens at the pathology
laboratory of Ain Shams University hospital and then
received neoadjuvant chemotherapy at the oncology
department of Ain Shams University. The protocol of
neoadjuvant chemotherapy was three or four cycles of
fluorouracil (500 mg/m2/21days), epirubicin (100 mg/
m2/21days) and cyclophosphamide (500 mg/m2/
21days). Thereafter, three to four cycles of docetaxel
(75–100 mg/m2/21days) were given. This was
followed by surgery either wide local excision,
simple, or radical mastectomy. All cases were
recruited in the period between 2017 and 2021.
Informed consent was obtained from all patients,
and the study was ethically approved by the research
ethical committee of Ain Shams University Hospital in
accordance with the 1964 Helsinki Declaration and its
later amendments.
Clinicopathological Features Evaluation
All patients’ data regarding age, menopausal state, and
imaging results for size of mass, laterality, and focality
were collected from patients’ medical records. Clinical
T (cT) was estimated according to the imaging results
and categorized according to the guidelines of AJCC
cancer staging manual (Hortobagyi et al., 2017).
Archival hematoxylin and eosin slides as well as
immunohistochemically stained slides for estrogen
(ER), progesterone (PR), Her2neu, and Ki67 were
retrieved. Examination was performed for
confirmation of the diagnosis, histopathologic type
of invasive component, the presence of ductal
carcinoma in situ (DCIS), and lymphovascular
invasion. All parameters were re-evaluated according
to the recent guidelines of WHO classification of
tumors of the breast (Rakha et al., 2019) and
(Fitzgibbons and Connolly, 2021).
Tumor-infiltrating lymphocyte (TILs) were classified
into low (<10%), intermediate (10-50%), and high
(more than 50%) groups. They identified a hotspot
within the tumor using a low-power field and the area
% of lymphocytes and plasma cells in both stromal and
intratumoral portions using a medium-power field
(×100) (Fujimoto et al., 2019). Nodal status either
positive or negative for metastatic deposits was
evaluated through core biopsy examination of any
enlarged suspicious axillary lymph node.
Immunohistochemistry
Immunohistochemistry was performed in cases when
immunohistochemical slides were unavailable, on the
core biopsy before therapy. Sections with a thickness of
4 μm were cut from paraffin blocks containing
formalin-fixed tumor tissue. The slides were stained
with the primary antibody (rabbit monoclonal anti
Ki67 human clone 30–09 Ventana Medical Systems)
utilizing a fully automated Benchmark Staining System
(Ventana Medical Systems, Oro Valley, Arizona,
USA).
Allred score was performed for ER and PR
immunostains considering the cutoff value between
Ki67 predictive value in breast cancer Abd El Khalek et al. 195
[Downloaded free from http://www.xep.eg.net on Wednesday, January 25, 2023, IP: 5.37.234.90]

negative expression and positive of 3/8. Her2neu score
was considered negative for scores 0 and 1. Positive
her2neu is considered in score 3. Cases with score 2
were re-evaluated with silver in-situ hybridization
(Fitzgibbons and Connolly, 2021)
Ki67 scoring system was performed according to the
Recommendations for Ki67 assessment in breast
cancer from the International Ki67 in Breast Cancer
Working Group (Dowsett et al., 2011). At least 3 high-
power (×40 objective) fields were selected to represent
the spectrum of staining seen on initial overview of the
whole section. Only nuclear staining is considered
positive. Staining intensity is not relevant. Scoring
involved the counting of at least 500–1000
malignant invasive cells (cases with small areas
malignant cells were excluded). Then the number of
positive nuclei was divided by the total number to
obtain overall score.
Molecular subtypes were then categorized into four
groups as follows: luminal A for ER+ and/or PR+,
HER2−, and low Ki67 tumors; luminal B for ER+ and/
or PR+ and HER2+ or ER+ and/or PR+, Her2−, and
high Ki67 tumors; Her2 enriched for ER−, PR−, and
Her2+ tumors; and TNBC negative for ER, PR, and
Her2 tumors (Hortobagyi et al., 2017).
Evaluation of pCR
Identification of pCR was confirmed upon
examination of the corresponding surgical specimens
of the 84 cases. It was defined by complete absence of
any invasive tumor cells from breast tissue as well as
regional lymph nodes regardless of the presence of
DCIS (Sahoo and Leste, 2019).
Statistical analysis
All data were processed by IBM SPSS statistics (V.
26.0, IBM Corp., USA, 2019) (SPSS Inc., Chicago,
Illinois, USA). Date were expressed as median and
percentiles for quantitative nonparametric measures.
Comparison between two independent groups for
nonparametric data was done using Wilcoxon rank-
sum test. The relationship of different
clinicopathologic parameter and pCR was analyzed
by the χ2
test or Fisher’s exact test. Receiver operating
characteristic (ROC) curve analysis was performed to
assess the predictive value for Ki67 expression.
Logistic stepwise multi-regression analysis was used
to search for a panel of the most independent
parameters that can predict the pCR. The
probability of error at 0.05 was considered
significant, whereas at 0.01 and 0.001 are highly
significant.
Results
Clinicopathologic features of invasive mammary
carcinoma cases
A total of 84 cases of breast carcinoma receiving
neoadjuvant therapy were included in the study. Of
84 cases, 21 (25%) showed pCR. Partial response (PR)
and no response (NR) were reported in 46/84 (54.8%)
and 17/84 cases (20.2%), respectively. pCR in, luminal
A, luminal B, Her2neu-enriched, and triple-negative
phenotypes were of 0/16 (0%), 7/27 (25.9%), 12/35
(34.3%), and 2/6 (33.3%) cases, respectively
The median age for all cases was 52 years, with the
range from 28 to 83 years. The median ages for pCR,
PR, and NR were 60, 48, and 55 years, respectively.
The median size of the mass estimated by
ultrasonography at the time of presentation was
2.25 cm with ranges from 1 to 5.5 cm. The median
size of masses in cases with pCR, PR, and NR were 2,
2.5, and 3 cm, respectively.
The most common type of mammary carcinoma was
invasive ductal carcinoma, NST, representing 95.2% of
all cases. Hormone-positive tumors including luminal
A and luminal B together represented 43 (51.2%) of 84
cases. Most cases represented with T1 and T2 tumor
stage at the time of diagnosis, being 63.1 and 48.8%,
respectively. Of 84 cases of invasive mammary
carcinoma, 12 cases (14.2%) showed positive nodal
metastatic deposits. Tumor-infiltrating lymphocytes
were low, intermediate, and high in 54.8, 26.2, and
19%, respectively. All clinicopathologic characteristics
are listed in Table 1.
Predictive value of Ki67 in invasive mammary
carcinoma cases
The predictive value of Ki67 was estimated using ROC
curve. This study showed a predictivevalue of 25% for all
cases, with a sensitivity of81%and a specificity of96.8%.
The area under the curve was 0.858 (P<0.001, 95CI:
0.775-0.942). Of 84 cases, 26 have Ki67 percentages
above 25%, the predictive value for Ki67 (Fig. 1).
Estimation of the predictive value using ROC curve for
each molecular subtype shows percentages of 22% in
Her2-enriched and 25% in luminal B subtypes (Fig. 2).
No complete response was detected in patients with
luminal A molecular subtype. Only 6 cases showed
triple negativity, which were insufficient for evaluation
using ROC curve.
The association between predictive clinicopathologic
factors and pCR
No significant association was found between pCR and
median of both age and size of nodule as revealed by
196 Egyptian Journal of Pathology, Vol. 41 No. 2, July-December 2021
[Downloaded free from http://www.xep.eg.net on Wednesday, January 25, 2023, IP: 5.37.234.90]

Wilcoxon rank sum test. However, comparing the
menopausal state and T stage regarding their
association with pathologic response revealed a
significantly higher rate of pCR in postmenopausal
status as well as low pathologic tumor stages (pT)
(P=0.014 and 0.005, respectively). No significant
association was found between pCR and tumor
laterality, focality, type, identified DCIS,
lymphovascular invasion, and molecular subtype. On
the contrary, pCR showed a significantly higher rate of
occurrence in association with higher Ki67 values and
TILs (<0.0001, each) and negative nodal status
(P=0.003). The association between predictive
clinicopathologic factors and pCR is shown in
Table 2.
Predicting factors of complete pathologic response
Among all of the studied cases, logistic stepwise multi-
regression analysis revealed that Ki67 value higher than
25% and high tumor-infiltrating lymphocytes were the
most independent predictive factors for pCR (Table 3).
Regarding Her2-enriched cases and luminal B
subtypes, Ki67 value and T stage before therapy for
the earlier and node stage and cT for the latter were the
most independent predictive factors for pCR. Only
model 3 of stepwise multi-regression for both subtypes
is displayed in Table 4.
Discussion
As has long been acknowledged, pCR after
neoadjuvant chemotherapy is well established as a
surrogate for beneficial long-term outcome (Wang-
Lopez et al., 2015; Pennisi et al., 2016). Neoadjuvant
chemotherapy was previously restricted to locally
advanced tumor or inflammatory breast carcinoma,
but now it is applied more extensively, as it has
several benefits, such as (1) converting an
unresectable, inoperable, locally advanced tumor to
an operable one; (2) downstaging operable tumors
can increase the likelihood of breast conservation
surgery; (3) providing prognostic information and
allowing for treatment changes or discontinuation in
the case of unresponsive tumors; and (4) providing an
ideal research setting for studying biomarkers and
intermediate end points (Pennisi et al., 2016).
In the current retrospective cohort study, the
pathologic response rate of 84 cases of breast
carcinoma that received neoadjuvant chemotherapy
was evaluated. We reported pCR in 25% of all the
cases.
Grover et al. (2021) and Rapoport et al. (2019) reported
33.8 and 45% pCR rates of the studied cases,
respectively. This disparity could be attributed to the
different proportion of each molecular subtype in
Table 1 Clinicopathologic parameters of the studied 84 cases
Clinicopathologic parameter (number of
cases=84)
Number of cases
(%)
Age, years
<50 36 (42.9)
≥50 48 (57.1)
Menopausal state
Postmenopausal 55(65.5)
Premenopausal 29 (34.5)
Laterality
Right side 38 (45.2)
Left side 46 (54.8)
Focality
Unifocal 77 (91.7)
multiple 7 (8.3)
Tumor type
IDC 80 (95.2)
ILC 3(3.6)
others 1 (1.2)
DCIS
Not identified 53 (63)
identified 31 (37)
Lymphovascular invasion
Not identified 75 (89.3)
identified 9 (10.7)
Tumor grade
Grade 1 4 (4.8)
Grade 2 68 (81%)
Grade 3 12 (14.4)
Molecular subtype
Luminal A 16 (19)
Luminal B 27 (32.1)
Her2 enriched 35 (41.7)
Triple negative 6 (7.1)
Ki67 value
<25 58 (69)
≥25 26 (31)
Tumor-infiltrating lymphocytes
low 46 (54.8)
Intermediate 22 (26.2)
high 16 (19)
cT stage at the time of presentation
T1 35 (20.8)
T2 41 (48.8)
T3 8 (16.7)
Nodal status at the time of presentation
Negative 72 (85.7)
Positive 12 (14.3)
Response to therapy
pCR 21 (25)
PR 46 (54.8)
NR 17 (20.2)
cT, clinical tumor stage; DCIS, ductal carcinoma in situ; IDC,
invasive duct carcinoma; ILC, invasive lobular carcinoma; LVI,
lymphovascular invasion; NR, no response; pCR, pathologic
complete response; PR, partial response.
Ki67 predictive value in breast cancer Abd El Khalek et al. 197
[Downloaded free from http://www.xep.eg.net on Wednesday, January 25, 2023, IP: 5.37.234.90]

different studies, inequal number of chemotherapy
cycles, or different treatment regimens.
Our study reported a pCR rate of 25.9% in the luminal
B cases, 34.3% in Her2-enriched cases, and 33.3% in
Fig. 2
ROC curve analysis showing the predictive performance of Ki67 for discriminating pCR from those without among all studied groups.
Fig. 1
Breast carcinoma cases showing Ki67 immunohistochemical expression. (a, b) Low KI67 proliferative rate (×200 and ×400, respectively). (c, d)
High Ki67 proliferative rate (×200 and ×400, respectively).
198 Egyptian Journal of Pathology, Vol. 41 No. 2, July-December 2021
[Downloaded free from http://www.xep.eg.net on Wednesday, January 25, 2023, IP: 5.37.234.90]

triple-negative (TN) cases. None of the luminal A cases
achieved pCR. The definition of pCR lacks uniformity,
and the prediction of outcome may vary according to
different biological subtypes (Pennisi et al., 2016).
Our results demonstrated that pCR is lower in luminal
B than in Her2-enriched and TN subtype. This is in
concordance with Grover et al. (2021) who reported
lower pCR in HR+ (13.8%) compared with TN
(45.5%) or HER2 enriched (52%). Another study
revealed that pCR was significantly elevated in the
HER2-enriched and TN subtypes (58.2% and 47.4%,
respectively) in relation to the luminal subtypes
(27.8%) (Li et al., 2016).
Omranipour et al. (2020) reported a pCR rate of 14.6%
in patients with ER+ / HER2− breast cancer which is
lower than our result. In the German population, von
Minckwitz et al. (2012) reported a pCR rate of 8.9 %
and 15.4% in luminal A and luminal B subtypes,
respectively.
Compared with the previous studies, the ACOSOG
Z1071 multicenter clinical trial with 317 cases reported
Table 2 Association between different clinicopathologic parameters and pathologic complete response
Clinicopathologic parameter (N=84) pCR, N (%) PR, N (%) NR, N (%) *χ2
Sig
Menopausal state
Postmenopausal (55) 16 (29.1) 24 (43.6) 15 (27.3) 8.563 0.014 (S)
Premenopausal (29) 5 (3.4) 22 (75.9) 2 (6.9)
Laterality
Right side (38) 16 (42.1) 21 (55.3) 9 (23.7) 5.456 0.065
Left side (46) 5 (10.9) 25 (54.3) 8 (17.4)
Focality
Unifocal (77) 21 (27.2) 49 (63.6) 7 (9.1) 6.308 0.177
Multiple (7) 0 7 (100) 0
Tumor type
IDC (80) 21 (26.3) 43 (53.8) 16 (20) 6.487 0.166
ILC (3) 0 3 (100) 0
Others (1) 0 0 1 (100)
DCIS
Not identified (52) 16 (30.8) 23 (44.2) 13 (25) 6.441 0.169
Identified (31) 5 (16.1) 22 (71) 4 (12.9)
Lymphovascular invasion
Not identified (75) 21 (28) 38 (50.7) 16 (21.3) 5.079 0.079
Identified (9) 0 8 (88.9) 1 (11.1)
Tumor grade
Grade 1/2 (72) 21 (29.2) 37 (51.4) 14 (19.4) 4.704 0.095
Grade 3 (12) 0 9 (75) 3 (25)
Molecular subtype
Luminal A (16) 0 12 (75) 4 (25) 9.867 0.13
Luminal B (27) 7 (25.9) 12 (44.4) 8 (29.6)
Her2 enriched (35) 12 (34.3) 19 (54.3) 4 (11.4)
Triple negative (6) 2 (33.3) 1 (16.7) 3 (50)
Ki67 value
<25 (58) 3 (5.2) 40 (69) 24 (41.3) 25.769 0.0001 (HS)
≥25 (26) 18 (69.2) 6 (23.1) 2 (7.7)
TILs
Low (46) 4 (8.7) 28 (60.9) 14 (30.4) 30.23 0.0001 (HS)
Intermediate (22) 5 (22.7) 14 (63.6) 3 (13.6)
High (16) 12 (75) 4 (25) 0
T stage at the time of presentation
T1 (35) 14 (40) 13 (37.1) 8 (22.9) 15.038 0.005 (HS)
T2 (41) 7 (17.1) 29 (70.7) 5 (12.2)
T3 (8) 0 4 (50) 4 (50)
Nodal status at the time of presentation
Negative (72) 21 (29.2) 34 (47.2) 17 (23.6) 11.565 0.003 (S)
Positive (12) 0 12 (100) 0
DCIS, ductal carcinoma in situ; IDC, invasive duct carcinoma; ILC, invasive lobular carcinoma; pCR, pathologic complete response; PR,
partial response; NR, no response; TILs, tumor-infiltrating lymphocytes. *χ2
test.
Ki67 predictive value in breast cancer Abd El Khalek et al. 199
[Downloaded free from http://www.xep.eg.net on Wednesday, January 25, 2023, IP: 5.37.234.90]

a reduced rate of pCR (11.4%) (Boughey et al., 2014).
Moreover, another study reported a 9% pCR rate
(Esserman et al., 2012), the same rate which was
achieved by Caudle et al. (2012) in patients with
HR+ / HER2− subtype. A much lower rate
(5% and 4.3%, respectively) was also reported in
HR+ / HER2− subtype (Lips et al., 2012; Petruolo
et al., 2017).
Table 3 Logistic stepwise multi-regression analysis for the most predictive factor for pathologic complete response in all the 84
studied cases
Predictive clinicopathologic parameters
Model 1
Item Reg. Coef. t P Sig. F-ratio P Sig.
(Constant) 0.062 0.25 0.803 NS
Age 0 0.177 0.86 NS
Ki67 0.66 8.755 0 HS
Grade −0.02 −0.324 0.747 NS
Focality 0.016 0.228 0.821 NS
Type −0.105 −1.131 0.262 NS
DCIS 0.002 0.043 0.966 NS
LVI −0.134 −1.474 0.145 NS
Luminal B 0.011 0.132 0.895 NS
Her2 enriched 0.252 3.75 0 HS
Triple negative −0.01 −0.089 0.929 NS
TIL 0.192 5.065 0 HS
Node status −0.171 −1.915 0.06 NS
cT −0.123 −2.659 0.01 S
24.195 0 HS
Model 2
Item Reg. Coef. t P Sig. F-ratio P Sig.
(Constant) −0.038 −0.447 0.656 NS
Ki67 0.613 10.083 0 HS
MS3 0.291 5.508 0 HS
TIL 0.211 6.615 0 HS
Node status −0.252 −3.58 0.001 HS
cT. ?0.151 ? 4.01 0 HS
65.075 0 NS
Model 3
Item Reg. Coef. t P Sig. F-ratio P Sig.
(Constant) -0.16 -2.634 0.01 S
Ki67 0.723 10.739 0 HS
TIL 0.15 4.166 0 HS
99.225 0 HS
cT, clinical tumor stage; DCIS, ductal carcinoma in situ; HS, highly significant; IDC, invasive duct carcinoma; ILC, invasive lobular
carcinoma; LVI, lymphovascular invasion; Reg. Coef, regression coefficiency.
Table 4 Stepwise multi-regression analysis for the most predictive factor for pathologic complete response in Her2-enriched and
luminal B molecular subtypes (model 3)
Model 3 for Her2-enriched cases
Item Reg. Coef. t P Sig. F-ratio P Sig.
(Constant) 0.205 2.018 0.052 NS
Ki67 0.909 13.572 0 HS
cT. −0.083 −1.705 0.098 NS
127.622 0 HS
Model 3 for luminal B cases Reg. Coef t P Sig F-ratio
Item
Constant 1.192 5.894 0 HS
Node status −0.558 −3.934 0.001 HS
cT before therapy −0.423 −4.371 0 HS
13.278 0 HS
cT, clinical tumor stage; DCIS, ductal carcinoma in situ; HS, highly significant; IDC, invasive duct carcinoma; ILC, invasive lobular
carcinoma; LVI, lymphovascular invasion; Reg. Coef, regression coefficiency.
200 Egyptian Journal of Pathology, Vol. 41 No. 2, July-December 2021
[Downloaded free from http://www.xep.eg.net on Wednesday, January 25, 2023, IP: 5.37.234.90]

A study was performed on patients with TNBC who
were divided into two groups according to the
chemotherapeutic agent. The pCR in both groups
was reported to be 41.8 and 50% (Gass et al., 2018).
Other studies reported a near pCR rate of 48%
(Jovanović et al., 2017; Georgy et al., 2021). These
results are higher than ours, and this discrepancy may
be owing to the limited number of TNBC in our study.
Oncologists can use predictive factors of chemotherapy
outcomes to determine whether neoadjuvant
chemotherapy is necessary. Because most
chemotherapy regimens have adverse side effects, it
is critical to avoid unneeded systemic chemotherapy
that causes other treatments to be delayed (Kim et al.,
2014). Many studies have reported that tumors with
more proliferative activity respond better to
chemotherapy and that Ki67 value can be used as a
predictive factor for a higher pCR rate (Yerushalmi
et al., 2010). Our study demonstrated that Ki67 is one
of the predictive factors of pathological response.
Calculation of the Ki67 cutoff value is a very important
step for accurate and meaningful results. The cutoff
value for Ki67 expression categorization in predicting
the response to neoadjuvant chemotherapy is
determined using ROC curve analysis. Because a
higher sensitivity is accompanied by a lower
specificity, and vice versa, there is always a trade-off
between sensitivity and specificity. The ideal cutoff
point was determined using ROC curve analysis, as it
had the largest sum of sensitivity and specificity (Kim
et al., 2014).
Using ROC curve analysis, we detected an optimal
cutoff point of 25% for Ki67 in all breast cancer cases.
This is agreeable with Kim et al., 2014, who reported
the same cutoff point using ROC curve (Kim et al.,
2014). Many other studies detected a cutoff value
ranging between 12 and 25% but without
explanation or depending on the median value
(Nishimura et al., 2010; Fasching et al., 2011; Li
et al., 2011).
We also reported a cutoff point of 22% and 25% for
Ki67 in Her2-enriched and luminal B subtypes,
respectively. Unfortunately, none of the luminal A
cases achieved pCR. Moreover, we had a limited
number of TNBC cases in our study, so ROC curve
analysis was not applicable.
In concordance with our results, Omranipour et al.
(2020) reported a 22.5% cutoff value of Ki67 in HR
+/HER? cases. Moreover, Kim et al. (2014) reported
that the 25% Ki67 expression cutoff value was useful for
predicting pCR, especially in the Her2-enriched
subgroup (P=0.019).
Arafah et al. (2021) reported a 30% cutoff value for
Ki67 in TNBC. A Ki67 value of more than 25% is
related to a higher mortality when compared with
lower expression rates (Petrelli et al., 2015).
Other predictive factors are reported by our study,
including TILs, T stage before therapy, lymph node
metastasis, and postmenopausal state. These factors
have a significant correlation with pCR. Many studies
reported a significant correlation between TILs and
pCR (Herrero-Vicent et al., 2017; El-Mahdy et al.,
2020).
It is recognized that high TIL tumors are significantly
and independently associated with pCR in breast
carcinoma treated with neoadjuvant chemotherapy
(Denkert et al., 2010). A meta-analysis of 13,100
patients across 23 studies reported that a high TIL
level is related to a significantly elevated pCR rate in
comparison with a low TIL level (Wang et al., 2016).
According to the subset analysis of the previous meta-
analysis, positive correlations between TILs and pCR
were consistently significant in TN and HER2-
enriched breast cancers. Similarly, marginal
significance between higher TILs level and elevated
pCR was found for the ER+ breast cancers (Mao et al.,
2014). These data may indicate that TILs are essential
to achieve pCR in chemotherapy treatment irrespective
of subtype.
In concordance with our results, Li et al. (2021)
reported a significant correlation between pCR and
tumor grade, TILs, and Ki67.
Our results showed a significant correlation between
pCR and absence of lymph node metastasis. This is in
concordance with Samiei et al. (2020) who reported
that pCR achieved after neoadjuvant therapy is
strongly associated with absence of lymph node
metastasis. These findings give information that may
be used in future clinical studies to determine if axillary
surgery can be safely avoided in these selected patients
when pathologic examination identifies a breast pCR.
Li et al. (2021) reported a significant correlation
between pCR and molecular subtype. Unfortunately,
we did not find any significance with this parameter.
We reported a significant correlation between pCR and
menopausal state and T stage of the tumor. Other
Ki67 predictive value in breast cancer Abd El Khalek et al. 201
[Downloaded free from http://www.xep.eg.net on Wednesday, January 25, 2023, IP: 5.37.234.90]

studies did not find a significant correlation (Sasanpour
et al., 2018).
No significant correlation was found between pCR and
laterality, focality, tumor type, presence of DCIS,
lymphovascular invasion, tumor grade, and molecular
subtype.
Our results of stepwise multi-regression analysis
showed that Ki67 and TILs were associated with an
increased rate of pCR after neoadjuvant therapy with a
high significant correlation.
These results are agreeable with Li et al. (2016) who
showed in their analysis that high Ki67 and TILs are
strongly correlated with pCR in patients with breast
cancer. In a multivariate analysis carried out by Mao
et al. (2014) in systematic review and metanalysis, they
found that TILs were still an independent predictive
factor for high pCR rate whether TILs were detected
in intratumoral or stromal compartments.
TILs and Ki67 levels, in addition to Nottingham
histologic grade and receptor status, could be
beneficial. The combination of all of these variables
may aid in better predicting response to neoadjuvant
chemotherapy and selecting patients for neoadjuvant
therapy (Li et al. (2016).
In the univariate analysis of clinical response, Kim et al.
(2014) found that a high Ki67 labelling index was the
sole significant parameter for predicting improved
clinical response to neoadjuvant chemotherapy. In
multiple logistic regression analysis, they found that
the Ki67 labelling index was the only statistically
significant as an independent predictor of a pCR.
Univariate analysis was performed by other researchers,
who came up with various conclusions. Grover et al.
(2021) showed in their analysis that high TIL density
as well as higher tumor grade are correlated with pCR.
Rapoport et al. (2019) detected many factors associated
with pCR such as molecular subtype, primary tumor
size, nodal disease, age, ER receptor status, PR
receptor status, Ki67, and tumor stage.
Conclusion
In patients with breast cancer, Ki67 expression with a
cutoff threshold of 25% could be used to predict the
probability of achieving a complete response to
neoadjuvant therapy. Furthermore, TILs are strongly
associated with pCR.
Financial support and sponsorship
Nil.
Conflicts of interest
There are no conflicts of interest.
References
Abdelaziz A, Shawki M, Shaaban A, Albarouki S, Rachid A, Alsalhani O, et al.
(2021). Breast cancer awareness among egyptian women and the impact of
caring for patients with breast cancer on family caregivers’ knowledge and
behaviour. Res Oncol 17: 1–8.
Andre F, Pusztai L (2006). Molecular classification of breast cancer: implica-
tions for selection of adjuvant chemotherapy. Nat Clin Pract Oncol 3:621-
–632.
Arafah MA, Ouban A, Ameer OZ, Quek KJ (2021). KI-67 LI expression in triple-
negative breast cancer patients and its significance. Breast Cancer
15:11782234211016977.
Boughey JC, McCall LM, Ballman KV, Mittendorf EA, Ahrendt GM, Wilke LG, et
al. (2014). Tumor biology correlates with rates of breast-conserving surgery
and pathologic complete response after neoadjuvant chemotherapy for
breast cancer: findings from the ACOSOG Z1071 (Alliance) Prospective
Multicenter Clinical Trial. Ann Surg 260:608–614.
Caudle AS, Yu TK, Tucker SL, Bedrosian I, Litton JK, Gonzalez-Angulo AM
(2012). Local-regional control according to surrogate markers of breast
cancer subtypes and response to neoadjuvant chemotherapy in breast
cancer patients undergoing breast conserving therapy. Breast Cancer
Res 14:R83.
Coates AS, Winer EP, Goldhirsch A, Gelber RD, Gnant M, Piccart-Gebhart M,
et al. (2015). Tailoring therapies-improving the management of early breast
cancer: St Gallen International Expert Consensus on the Primary Therapy of
Early Breast Cancer 2015. Ann Oncol 26:1533–1546.
Denkert C, Loibl S, Noske A, Roller M, Müller BM, Komor M, et al. (2010).
Tumour-associated lymphocytes as an independent predictor of response to
neoadjuvant chemotherapy in breast cancer. J Clin Oncol 28:105–113.
Dowsett M, Nielsen TO, A’Hern R, Bartlett J, Coombes RC, Cuzick J, et al.
(2011). Assessment of Ki67 in breast cancer: recommendations from the
International Ki67 in Breast Cancer working group. J Natl Cancer Inst
103:1656–1664.
Early Breast Cancer Trialists’ Collaborative Group (EBCTCG) (2018). Long-
term outcomes for neoadjuvant versus adjuvant chemotherapy in early
breast cancer: meta-analysis of individual patient data from ten randomised
trials. Lancet Oncol 19:27–39.
El-Mahdy M, Ibrahim R, Hamed R, Elkady M, Bayoumy W, Elsayed Z, et al.
(2020). Prediction of response to neoadjuvant chemotherapy in egyptian
patients with locally advanced breast cancer: the evolving role of tumour
infiltrating lymphocytes (TILs). J Cancer Ther 11:206–219.
Esserman LJ, Berry DA, Cheang MC, Yau C, Perou CM, Carey L, et al. (2012).
I-SPY 1 TRIAL Investigators. Chemotherapy response and recurrence-free
survival in neoadjuvant breast cancer depends on biomarker profiles: results
from the I-SPY 1 TRIAL (CALGB 150007/150012; ACRIN 6657). Breast
Cancer Res Treat 132:1049–1062.
Fasching PA, Heusinger K, Haeberle L, Niklos M, Hein A, Bayer CM, et al.
(2011). Ki67, chemotherapy response, and prognosis in breast cancer
patients receiving neoadjuvant treatment. BMC Cancer 11:486.
Fitzgibbons PL, Connolly JL (2021). Template for Reporting Results of Bio-
marker Testing of Specimens from Patients with Carcinoma of the Breast
With guidance from the CAP Cancer and CAP Pathology Electronic Report-
ing Committees ©. College of American Pathologists (CAP).
Fujimoto Y, Watanabe T, Hida AI, Higuchi T, Miyagawa Y, Ozawa H, et al.
(2019). Prognostic significance of tumor-infiltrating lymphocytes may differ
depending on Ki67 expression levels in estrogen receptor-positive/HER2-
negative operated breast cancers. Breast Cancer 26: 738–747.
Gass P, Lux MP, Rauh C, Hein A, Bani MR, Fiessler C, et al. (2018). Prediction
of pathological complete response and prognosis in patients with neoadju-
vant treatment for triple-negative breast cancer. BMC Cancer 18:1051.
Georgy JT, Singh A, John AO, Joel A, Andrews AG, Thumaty DB, et al. (2021).
Pathological response and clinical outcomes in operable triple-negative
breast cancer with cisplatin added to standard neoadjuvant chemotherapy.
Klin Onkol 34:49–55.
Goldhirsch A, Wood WC, Coates AS, Gelber RD, Thurlimann B, Senn HJ
(2011). Strategies for subtypes–dealing with the diversity of breast cancer:
202 Egyptian Journal of Pathology, Vol. 41 No. 2, July-December 2021
[Downloaded free from http://www.xep.eg.net on Wednesday, January 25, 2023, IP: 5.37.234.90]

highlights of the St. Gallen international expert Consensus on the primary
therapy of early breast cancer 2011. Ann Oncol 22:1736–1747.
Grover P, Phan K, Magge T, Chou A, Naffouje R, Jandarov R (2021).
Association of tumor infiltrating lymphocytes with pathological response after
neoadjuvant therapy in breast cancer: a retrospective study. J Clin Oncol
39:15e12621.
Hayes DF (2012). Targeting adjuvant chemotherapy: a good idea that needs to
be proven! J Clin Oncol 30:1264–1267.
Herrero-Vicent C, Guerrero A, Gavilá J, Gozalbo F, Hernández A, Sandiego S,
et al. (2017). Predictive and prognostic impact of tumor-infiltrating lympho-
cytes in triple-negative breast cancer treated with neoadjuvant chemothera-
py. Cancer 11:759.
Hortobagyi GN, Connolly JL, D’Oris CJ, Edge SB, Mittendorf EA, Rugo HS, et
al. (2017). Breast. In: Amin MB, Edge SB, Greene FL, Byrd DR, Brookland
RK, Washington MK, et al. editors. American Joint Committee on cancer,
AJCC cancer staging manual, 8th ed. New York: Springer pp. 589–628. P
Ibrahim AS, Khaled HM, Mikhail NN, Baraka H, Kamel H (2014). Cancer
incidence in Egypt: results of the National Population-Based Cancer Registry
Program. J Cancer Epidemiol 2014:437971.
Jovanović B, Mayer IA, Mayer EL, Abramson VG, Bardia A, Sanders ME, et al.
(2017). A randomized phase II neoadjuvant study of cisplatin, paclitaxel with
or without everolimus in patients with stage II/III triple-negative breast cancer
(TNBC): responses and long-term outcome correlated with increased fre-
quency of DNA damage response gene mutations, TNBC subtype, AR
status, and Ki67. Clin Cancer Res 2017; 23:4035–4045.
Kim KI, Lee KH, Kim TR, Chun YS, Lee TH, Park HK (2014). Ki-67 as a predictor
of response to neoadjuvant chemotherapy in breast cancer patients. J Breast
Cancer 17:40–46.
Kumar V, Abbas AK, Aster JC, Robbins SL (2013). Robbins Basic Pathology.
Philadelphia: Elsevier/Saunders.
Lee H, Ko H, Seol H, Noh DY, Han W, Kim TY, et al. (2013). Expression of
immunohistochemical markers before and after neoadjuvant chemotherapy
in breast carcinoma, and their use as predictors of response. J Breast Cancer
16:395–403.
Li XR, Liu M, Zhang YJ, Wang JD, Zheng YQ, Li J, Ma B, Song X (2011).
Evaluation of ER, PgR, HER-2, Ki-67, cyclin D1, and nm23-H1 as predictors
of pathological complete response to neoadjuvant chemotherapy for locally
advanced breast cancer. Med Oncol 28:31–38.
Li XB, Krishnamurti U, Bhattarai S, Klimov S, Reid MD, O’Regan R, Aneja R
(2016). Biomarkers predicting pathologic complete response to neoadjuvant
chemotherapy in breast cancer. Am J Clin Pathol 145:871–878.
Li F, Yang Y, Wei Y, He P, Chen J, Zheng Z, et al. (2021). Deep learning-based
predictive biomarker of pathological complete response to neoadjuvant
chemotherapy from histological images in breast cancer. J Transl Med
19:348.
Lips E, Mulder L, De Ronde J, Mandjes I, Vincent A, Peeters MV, et al. (2012).
Neoadjuvant chemotherapy in ER+ HER2− breast cancer: response predic-
tion based on immunohistochemical and molecular characteristics. Breast
Cancer Res Treat 131:827–836.
Mao Y, Qu Q, Zhang Y, Liu J, Chen X, Shen K (2014). The value of tumour
infiltrating lymphocytes (TILs) for predicting response to neoadjuvant che-
motherapy in breast cancer: a systematic review and meta-analysis. PLoS
One 9:e115103.
Mukai H, Yamaguchi T, Takahashi M, Hozumi Y, Fujisawa T, Ohsumi S, et al.
(2020). Ki-67 response-guided preoperative chemotherapy for HER2-posi-
tive breast cancer: results of a randomised phase 2 study. Br J Cancer
122:1747–1753.
Nielsen TO, Leung SCY, Rimm DL, Dodson A, Acs B, Badve S, et al. (2021).
Assessment of Ki67 in breast cancer: Updated Recommendations from the
International Ki67 in Breast Cancer Working Group. J Natl Cancer Inst
113:808–819.
Nishimura R, Osako T, Okumura Y, Hayashi M, Arima N (2010). Clinical
significance of Ki-67 in neoadjuvant chemotherapy for primary breast cancer
as a predictor for chemosensitivity and for prognosis. Breast Cancer 17:269-
–275.
Omranipour R, Jalili R, Yazdankhahkenary A, Assarian A, Mirzania M, Eslami B
(2020). Evaluation of pathologic complete response (pCR) to neoadjuvant
chemotherapy in Iranian breast cancer patients with estrogen receptor
positive and HER2 negative and impact of predicting variables on pCR.
Eur J Breast Health 16:213–218.
Pennisi A, Kieber-Emmons T, Makhoul I, Hutchins L (2016). Relevance of
pathological complete response after neoadjuvant therapy for breast cancer.
Breast Cancer 10:103–106.
Petrelli F, Viale G, Cabiddu M, Barni S (2015). Prognostic value of
different cut-off levels of KI 67 in breast cancer: a systematic review and
meta-analysis of 64,196 patients. Breast Cancer Res Treat 153:477–
491.
Petruolo OA, Pilewskie M, Patil S, Barrio AV, Stempel M, Wen HY, et al. (2017).
Standard pathologic features can be used to identify a subset of estrogen
receptor-positive, HER2 negative patients likely to benefit from neoadjuvant
chemotherapy. Ann Surg Oncol 24:2556–2562.
Rakha EA, Allison KH, Ellis IO, Horii R, Masuda S, Penault-Liorca F, et al.
(2019). Invasive breast carcinoma. In: Allison KH, Ellis IO, Edi B, et al. Who
Classification of head and neck tumours. 5th ed. Lyon: IARC. pp. 82–
100.
Rapoport BL, Barnard-Tidy J, Van Eeden RI, Smit T, Nayler S, Benn C.
Pathological complete response in early breast cancer patients undergoing
neoadjuvant chemotherapy: Focus on Ki-67 and molecular subtypes. Ann
Oncol 2019; 30:III37.
Sahoo S, Leste SC (2019). Pathology of breast carcinomas after neoadjuvant
chemotherapy. an overview with recommendations on specimen processing
and reporting. Arch Pathol Lab Med 133:633–642.
Samiei S, van Nijnatten TJA, de Munck L, Keymeulen KBMI, Simons JM,
Kooreman LFS, et al. (2020). Correlation between pathologic complete
response in the breast and absence of axillary lymph node metastases after
neoadjuvant systemic therapy. Ann Surg 271:574–580.
Sasanpour P, Sandoughdaran S, Mosavi-Jarrahi A, Malekzadeh M (2018).
Predictors of pathological complete response to neoadjuvant chemotherapy
in iranian breast cancer patients. Asian Pac J Cancer Prev 19:2423–2427.
Siegel RL, Miller KD, Fuchs H, Jemal A (2021). Cancer statistics, 2021. CA
Cancer J Clin 71:7–33.
Sørlie T, Perou CM, Tibshirani R, Aas T, Geisler S, Johnsen H, et al. (2001)
Gene expression patterns of breast carcinomas distinguish tumor sub-
classes with clinical implications. Proc Natl Acad Sci USA 98:10869–10874.
von Minckwitz G, Untch M, Blohmer JU, Costa SD, Eidtmann H, Fasching PA
(2012). Definition and impact of pathologic complete response on prognosis
after neoadjuvant chemotherapy in various intrinsic breast cancer subtypes.
J Clin Oncol 30:1796–1804.
Wang K, Xu J, Zhang T, Xue D (2016). Tumor-infiltrating lymphocytes in breast
cancer predict the response to chemotherapy and survival outcome: a meta-
analysis. Oncotarget 7:44288–44298.
Wang-Lopez Q, Chalabi N, Abrial C, Radosevic-Robin N, Durando X, Mouret-
Reynier MA, et al. (2015). Can pathologic complete response (pCR) be used
as a surrogate marker of survival after neoadjuvant therapy for breast
cancer?. Crit Rev Oncol Hematol 95:88–104.
Wirapati P, Sotiriou C, Kunkel S, Farmer P, Pradervand S, Haibe-Kains B, et al.
(2008). Meta-analysis of gene expression profiles in breast cancer: toward a
unified understanding of breast cancer subtyping and prognosis signatures.
Breast Cancer Res 10:R65.
Yerushalmi R, Woods R, Ravdin PM, Hayes MM, Gelmon KA (2010). Ki67 in
breast cancer: prognostic and predictive potential. Lancet Oncol 11:174–
183.
Ki67 predictive value in breast cancer Abd El Khalek et al. 203
[Downloaded free from http://www.xep.eg.net on Wednesday, January 25, 2023, IP: 5.37.234.90]