CD133 is an independent predictive and prognostic marker in metastatic breast cancer

Abstract

BACKGROUND:

CD133 is a transmembrane glycoprotein and is considered the most common cell surface marker to identify cancer stem cells in hematological and solid tumors, including breast cancer.

OBJECTIVES:

To evaluate the impact of immunohistochemical expression of CD133 on response rate and survival in metastatic breast cancer, as well as to correlate it with various demographics and clinicopathological characteristics.

METHODS:

One-hundred metastatic breast cancer patients were prospectively recruited at the Medical Oncology Department at South Egypt Cancer Institute during the period from January 2018 to January 2020.

RESULTS:

There was a statistically significant correlation between CD133 positive patients with various adverse clinicopathological parameters such as high grade ( $p=$ 0.013), higher tumor ( $p=$ 0.001), and nodal staging ( $p=$ 0.024) during a median follow-up time of 17 months. In addition, cases with CD133 positive expression had a significantly lower survival time than those with negative expression (3-years OS 37.4% versus 85.5%, $p=$ 0.024). Regarding the response rate, CD133 positive patients had a lower response rate than negative patients (50% versus 54%, $p=$ 0.012).

CONCLUSIONS:

Positive CD133 is correlated with poor prognosis in metastatic breast cancer patients.

Keywords

Metastatic breast cancer CD133 expression clinicopathological characteristics survival and response rate cancer stem cells

1. Introduction

Breast cancer (BC) is the most prevalent cancer and the second leading cause of cancer-related mortality after bronchogenic carcinoma in females worldwide [1]. In Egypt, breast cancer accounts for 18.9% of all cancers, with 5-year survival rates of about 97% and 20% for early and metastatic stages, respectively [2].

Metastatic breast cancer is an incurable disease with a median survival duration of about two years [3]. The prognosis is determined by several factors like estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), patient age and performance status, as well as the number of metastatic sites [4, 5].

Despite advancements in diagnosis and treatment, the poor response rate in metastatic disease necessitates more research into novel prognostic and predictive factors. CD133 is a transmembrane glycoprotein and is considered the most common cell surface marker to identify cancer stem cells (CSCs), which was initially considered as a marker of hematopoietic stem cells [6, 7]. CD133 was also detected in CSCs of different solid tumors like bronchogenic, CNS, colon, and pancreatic cancers [8, 9, 10, 11]. CSCs have been implicated as the source of cancer initiation, proliferation, recurrence, metastasis, and chemotherapy resistance [10, 12, 13, 14]. Targeting breast cancer stem cells may improve cancer therapy [15].

Several studies have been conducted to study the association between CD133 expression and breast cancer prognosis; nonetheless, the findings have been contradictory [16, 17, 18, 19, 20]. This disparity could be attributed to diversity in previous studies that included different TNM stages (from stage I to IV), which were considered a limitation in these studies that could have influenced the results. To our knowledge, there are no published studies which focused only on CD133 expression on the metastatic breast cancer patients. The metastatic group was considered an exclusion criterion in the majority of previous studies.

So, we conducted this prospective study to evaluate the impact of immunohistochemical expression of CD133 on response rate and survival in metastatic breast cancer and to correlate it with various demographics and clinicopathological characteristics.

2. Materials and methods

2.1 Study design and patients

A prospective observational study was conducted on 100 de novo metastatic BC patients who had stage IV breast cancer at the initial presentation according to American Joint Committee on Cancer’s Staging System for Breast Cancer, Eighth Edition [21] at the Medical Oncology Department, South Egypt Cancer Institute, Assiut University during the period from January 2018 to January 2020. The inclusion criteria were histological diagnosis of breast carcinoma, age $\geqslant$ 18 years with performance status $\leqslant$ 2, and adequate hematological, liver, and renal functions. Patients with incomplete documents, pregnant, lactating patients, and those with synchronous cancer and cancer during the past five years were excluded. Ethical approval was obtained from our institutional ethical committee SECI-IRB by number IORG0006563-468.

2.2 Assessment of breast cancer status

All BC patients were subjected to appropriate baseline assessment, including history, clinical examination, radiological diagnosis (CT/MRI $\pm$ bone scan), and pathological data. Patients were subjected to different lines of systemic treatment, including chemotherapy with various protocols (AC, FAC, FEC, Taxanes, Gemcitabine plus either carboplatin or paclitaxel, Vinorelbine plus capecitabine, and liposomal doxorubicin), hormonal treatment (Tamoxifen and aromatase inhibitors) $\pm$ targeted therapy (Trastuzumab) depending on the physician’s decision. Treatment response reassessment was done according to Response Evaluation Criteria in Solid Tumors (RECIST) v. 1.1 guidelines [22]. Survival outcomes were defined as follows: overall survival (OS), time from the date of treatment initiation to the date of death from all causes; progression-free survival (PFS), defined as the time interval from the date of treatment initiation to the date of disease progression or the date of death. Best overall response rate (BOR) was defined as the percentage of patients with the best response obtained throughout the study with complete response (CR), complete response indeterminate (CRi), or partial response (PR) as determined by the investigator.

When the Allred score was $\geqslant$ 2, ER and PR were considered positive by IHC technique. Her-2 was considered positive if strong complete membranous staining in $>$ 10% of tumor cells (score 3) or Her2 amplification by FISH which applied for cases with equivocal Her2 IHC (score 2).

In terms of the evaluation of tumor-infiltrating lymphocytes (TILs), this study was stratified breast carcinoma based on the percentage of stromal mononuclear immune cells into three categories: mild TILs ( $<$ 40%), moderate TILs (40–50%), and dense TILs ( $>$ 50%) [23].

2.3 CD133 immunohistochemistry

The formalin-fixed paraffin-embedded (FFPE) tissue blocks were obtained from the archives of the Oncologic Pathology Department, SECI. Each block was cut into 4 $\mu$ m thick sections and mounted on positively charged slides. The sections were deparaffinized before being gradually rehydrated in alcohol. In a hot water bath at 90 ${}^{\circ}$ C for 45 minutes, antigen was extracted using high PH (9) Tris EDTA. A hydrogen peroxide block was applied for 10 min. The primary rabbit monoclonal anti-CD133/Prominin-1 (PROM1) [clone MD49R] antibody (Catalog # RM0202, Medaysis Co., San Francisco Bay Area, USA) was applied into tumor tissue sections at a dilution of 1/100 (optimum dilution according to the datasheet and best results). The tissue slices were then incubated for 1 hour at room temperature in a humid chamber. The universal staining that we applied was a SensiTek HRP (Anti-Polyvant) Conjugate (Ready-To-Use) (ScyTek Laboratories, USA, 84323) applied at room temperature for 10 minutes following the manufacturer’s instructions. Streptavidin was applied to the slides for 10 minutes, followed by 5 minutes of diaminobenzidine (DAB) solution. A counterstain for the tissue sections was performed using Mayer’s Hematoxylin. As a positive control, CD133-positive colon cancer tissues were used. Sections of the tissue-specific positive controls were stained using the same protocol but without the primary antibody as a negative control.

2.4 Evaluation of CD133 expression

An unequivocal membraneous brownish staining with or without cytoplasmic staining of CD133 is considered positive if $\geqslant$ 10% of tumor cells were stained. Also, a modified H-score method was used. For each tumor case, the evaluation of both intensity and percentage positivity were done, separately. The intensity score was scaled (0–3), corresponding to negative, weak, moderate, and strong. Finally, the intensity of staining was multiplied by the percentage (0–100), and the final score was calculated with a scale (0–300) [24].

2.5 Statistical analysis

Statistical analysis of data was performed using SPSS (Statistical Package for the Social Science; SPSS Inc., Chicago, IL, USA) version 22. Data were statistically expressed as mean $\pm$ standard deviation ( $\pm$ SD), or median and range when not normally distributed, frequencies (number of cases), and relative frequencies (percentages) for non-normally distributed data. Comparison of quantitative variables between the study groups was made using student $t$ -test for normally distributed data and Mann Whitney U test for data non normally distributed. The Chi-square ( $\chi^{2}$ ) test was used to compare categorical data. The exact test was used instead when the expected frequency was less than 5. The different independent variables were measured using the odds ratio (OR) with a 95% CI and Logistic regression. Kaplan-Meier’s method with log-rank test or Cox regression method for univariate or multivariate overall and progression-free survival analysis was used to assess the associations among the positive staining of CD133 and clinicopathological indices. $P$ -value is constantly tailed set significant at the 0.05 level.

3. Results

3.1 Demographics and clinicopathological characteristics

The median age for the entire cohort was 47 years (range 26–76 years), and 58% of them were premenopausal. Fifty-four patients (54%) had an oligometastatic disease, and forty-six patients (46%) had multiple metastases. The most common metastatic site in general (including both oligometastatic and multiple metastatic diseases) was the bone (60%) followed by the lung (49%), liver (17%), pleura (12%), and the brain (2%). Seventy-five percent of patients had visceral metastases. Fifty-seven percent had tumor stage T1/T2, 30% of patients had T3 and 13% had T4. Sixty-six percent of patients had N2/N3 and 34% had N0/N1 disease. Eighty-one percent had grade II and 19% had grade III disease. In terms of pathological subtypes, 95 patients had invasive ductal carcinoma (IDC), while other pathological subtypes described as two patients had invasive lobular carcinoma (ILC), one patient with invasive papillary carcinoma, 1 patient IDC with atypical medullary feature and 1 patient had IDC with extensive neuroendocrine differentiation. ER, PR, and HER2 positivity were detected in 70%, 61%, and 43% of BC patients, respectively. Eleven patients were diagnosed with triple-negative breast cancer (TNBC). The detailed demographics and clinicopathological data are shown in Table 1.

Table 1
Demographic, clinical and pathological characteristics according to CD133 tumor marker ( $n=$ 100)

Variables	Number	Percent	CD133 tumor marker
			Negative ( $n=$ 35)		Positive ( $n=$ 65)		$p$ -value
			$N$	(%)	$N$	(%)
Age (years)
$<$ 50	54	(54.0)	19	(54.3)	35	(53.8)	0.966
$\geqslant$ 50	46	(46.0)	16	(45.7)	30	(46.0)
Menstruation status
Premenopausal	58	(58.0)	19	(54.3)	39	(60.0)	0.581
Postmenopausal	42	(42.0)	16	(45.7)	26	(40.0)
Performance status
1	95	(95.0)	34	(97.1)	61	(93.8)	0.655
2	5	(5.0)	1	(2.9)	4	(6.2)
Type of metastasis
Oligometastasis	54	(54.0)	23	(65.7)	31	(47.0)	0.085
Bone	26	(26.0)	12	(34.3)	14	(21.5)
Lung	13	(13.0)	3	(8.6)	10	(15.4)
Brain	2	(2.0)	0	(0.0)	2	(3.1)
Pleural	7	(7.0)	4	(11.4)	3	(4.6)
Liver	6	(6.0)	4	(11.4)	2	(3.1)
Multiple metastasis	46	(46.0)	12	(34.3)	34	(52.3)
Metastasis per site
Bone	60	(60.0)	21	(60.0)	39	(60.0)	1
Lung	49	(49.0)	12	(34.3)	37	(56.9)	0.031
Liver	17	(17.0)	7	(20.0)	10	(15.4)	0.540
Pleural	12	(12.0)	5	(14.3)	7	(10.8)	0.748
Brain	2	(2.0)	0	(0.0)	2	(3.1)	0.540
Distant lymph node	10	(10.0)	3	(8.6)	7	(10.8)	1
Visceral	75	(75.0)	24	(68.6)	51	(78.5)	0.276
Non visceral	25	(25.0)	11	(31.4)	14	(21.5)	0.276
Visceral crisis	17	(17.0)	3	(8.6)	14	(21.5)	1
Tumor grade
Grade II	81	(81.0)	33	(94.3)	48	(78.3)	0.013 ${}^{*}$
Grade III	19	(19.0)	2	(5.7)	17	(26.2)
Pathological type
IDC	95	(95.0)	35	(100.0)	60	(92.3)	0.159
Other pathology	5	(5.0)	0	(0.0)	5	(8.1)
Tumor size
T1&T2	57	(57.0)	28	(80.0)	29	(44.6)	0.001 ${}^{*}$
T3&T4	43	(43.0)	7	(20.0)	36	(55.4)
LN metastasis
N0&N1	34	(34.0)	17	(48.6)	17	(26.2)	0.024 ${}^{*}$
N2&N3	66	(66.0)	18	(51.4)	48	(73.8)
Lympho-vascular invasion
No	60	(60)	20	(57.1)	40	(61.5)	0.669
Yes	40	(40)	15	(42.9)	25	(38.5)
Peri-neural invasion
No	62	(62.0)	22	(62.9)	40	(61.5)	0.897
Yes	38	(38.0)	13	(37.1)	25	(38.5)
Tumor infiltrating lymphocyte
Mild stromal TILS	48	(48.0)	17	(48.6)	31	(47.7)	1
Moderate stromal TILS	47	(47.0)	16	(45.7)	31	(47.7)
Dense stromal TILS	5	(5.0)	2	(5.7)	3	(4.6)
Presence of DCIS	47	(47.0)	21	(60.0)	26	(40.0)	0.056
ER
Negative	30	(30.0)	11	(31.4)	19	(29.2)	0.819
Positive	70	(70.0)	24	(68.6)	46	(70.8)
PR
Negative	39	(39.0)	12	(34.3)	27	(41.5)	0.478
Positive	61	(61.0)	23	(65.7)	38	(58.5)

Table 1, continued
Variables	Number	Percent	CD133 tumor marker
			Negative ( $n=$ 35)		Positive ( $n=$ 65)		$p$ -value
			$N$	(%)	$N$	(%)
Her2neu
No	57	(57.0)	14	(40.0)	43	(66.2)	0.012 ${}^{*}$
Yes	43	(43.0)	21	(60.0)	22	(33.8)
Triple negative
No	89	(89.0)	33	(94.3)	56	(86.2)	0.320
Yes	11	(11.0)	2	(5.7)	9	(13.8)
Type of patients therapy
Chemotherapy $\pm$ target therapy
${}^{**}$ First line*	88	(88.9)	29	(33.0)	59	(67.0)	0.150
${}^{**}$ Second line*	44	(57.1)	14	(31.9)	30	(68.1)	0.375
${}^{**}$ Third line*	19	(57.5)	5	(26.3)	14	(73.7)	0.111
Hormonal
${}^{**}$ First line*	11	(11.1)	6	(54.5)	5	(45.5)	0.182
${}^{**}$ Second line*	33	(42.9)	13	(39.3)	20	(60.7)	1
${}^{**}$ Third line*	14	(42.5)	6	(42.9)	8	(57.1)	1

Qualitative data are presented as $n$ (%). Chi-square test or Fisher Exact test were used to compare categorical data. ${}^{*}$ Significance defined by $p<$ 0.05.

3.2 Correlation between CD133 expression and clinicopathological characteristics

Immunohistochemical (IHC) patterns of CD133 expression were examined in core needle biopsy (CNB) specimens from 100 patients, and it was found that 65% and 35% of patients had CD133-positive and negative expression, respectively. Expression of CD133 in tumor cells in invasive duct carcinoma of the breast is depicted in Fig. 1.

Figure 1.

Expression of CD133 in tumor cells in invasive duct carcinoma of the breast. (a) Strong membraneous and cytoplasmic expression of CD133 in tumor cells ( $\times$ 40). (b) Moderate membraneous expression of CD133 in tumor cells ( $\times$ 10). (c) Weak expression of CD133 in tumor cells ( $\times$ 20). (d) The tumor cells were negative for CD133 ( $\times$ 40).

CD133 positive expression was significantly correlated with grade III ( $p=$ 0.013), tumor stage T3/T4 ( $p=$ 0.001), N2/N3 ( $p=$ 0.024), and surprisingly, the occurrence of lung metastases ( $p=$ 0.031). Nevertheless, there was no statistically significant relationship between CD133 expression and other variables like age, menopausal status, ER, PR, Her2, and TNBC cases.

High H-score expression of CD133 was statistically significant with advanced (T) stage ( $P=$ 0.013) with median (range) [180 (60–300)] in advanced stage (T3/T4). The other clinicopathological parameters revealed no significant association with CD133 by the H-score evaluation method.

3.3 Response rate and CD133 expression

In this study, 91% of patients received chemotherapy during their disease course either as first-line or added as the second line after endocrine therapy failure, while 51% of patients received endocrine therapy.

CD133 positive expression was associated with a low response rate [partial response as the best overall response (BOR)] (50% versus 54%, $p=$ 0.012) and a higher rate of progressive disease (25% versus 2.9%, $p=$ 0.012) than those with negative expression. The association between the CD133 tumor marker and the best overall response rate of studied participants for the different lines of treatment is depicted in Table 2.

Figure 2.

Survival outcomes according to CD133 expression. (a) Overall survival according to CD133 tumor biomarker. (b) Progression-free survival according to CD133 tumor biomarker.

3.4 Survival outcomes in relation to CD133 expression

Our study demonstrated poor PFS (2-years PFS 19.1% versus 38.7%, $p=$ 0.065) and OS (3-years OS 37.4% versus 85.5%, $p=$ 0.024) for BC patients with CD133 positive expression in comparison with those with negative expression with a median follow up period of 17 months for the whole study cohort. Table 3 shows a survival study (PFS and OS) based on CD133.

Table 2
The association between the CD133 tumor marker and the best overall response rate (BOR) of studied participants for the different lines of treatment ( $n=$ 99)

Best overall response	RD	19	(54.3)	32	(50.0)	$P$ value
(BOR) ( $n=$ 99)	SD	15	(42.9)	16	(25.0)	0.012 ${}^{*}$
	PD	1	(2.9)	16	(25.0)

Logestic regression analysis.

Table 3

Survival analysis (PFS and OS) according to the CD133

	OS (3 years)		PFS (2 years)
	Estimate $\pm$ SE	$P$ value	Estimate $\pm$ SE	$P$ value
CD133
Negative	85.5 $\pm$ 6.9%	0.024 ${}^{*}$	38.7 $\pm$ 9.6%	0.065
Positive	37.4 $\pm$ 12.4%		19.1 $\pm$ 6.2%

Kaplan-Meier’s method with log-rank test was used for PFS and OS analysis. ${}^{*}$ Significance set at $p<$ 0.05.

Table 4

Univariate and multivariate analysis of 100 patients with BC according to clinic-pathological variables

	OS			PFS			Best overall response
	$p$ -value	HR	95% C.I. for HR	$p$ -value	HR	95% C.I. for HR	$p$ -value	OR	95% C.I. for OR
Univariate analysis
Age	0.061	2.099	0.966–4.557	0.439	1.231	0.744–1.980	0.293	0.569	0.199–1.627
Menopausal status	0.436	1.360	0.628–2.943	0.494	1.189	0.724–1.952	0.275	0.532	0.172–0.1.649
Tumor grade	0.881	0.933	0.375–2.317	0.939	0.977	0.541–1.764	0.158	0.222	0.028–1.791
Tumor size	0.947	1.026	0.477–2.208	0.077	0.647	0.400–1.048	0.018 ${}^{*}$	3.948	1.269–12.281
Lymph node metastasis	0.657	0.827	0.358–1.911	0.245	0.737	0.440–1.233	0.308	1.875	0.561–6.271
CD133	0.033 ${}^{*}$	0.347	0.131–0.919	0.078	0.619	0.363–1.056	0.021 ${}^{*}$	11.333	1.434–89.591
ER	0.015 ${}^{*}$	2.562	1.202–5.458	0.030 ${}^{*}$	1.728	1.055–2.830	0.023 ${}^{*}$	0.287	0.098–0.842
PR	0.051	2.183	0.997–4.781	0.081	1.535	0.948–2.486	0.005 ${}^{*}$	0.193	0.062–0.606
HER2/neu	0.331	1.494	0.665–3.356	0.620	0.887	0.552–1.426	0.459	0.663	0.224–1.965
Luminal A	0.325	1.486	0.675–3.275	0.130	1.498	0.922–2.434	0.129	0.417	0.135–1.289
Luminal B	0.161	2.147	0.738–6.245	0.862	0.955	0.565–1.611	0.633	0.744	0.220–2.512
HER2/neu overexpression	0.799	0.881	0.331–2.343	0.472	0.801	0.436–1.469	0.855	1.137	0.286–4.524
Triple negative	0.001 ${}^{*}$	0.213	0.087–0.525	0.033 ${}^{*}$	0.478	0.242–0.943	0.001 ${}^{*}$	10.636	2.587–43.736
Multivariate analysis
CD133	0.013 ${}^{*}$	0.281	0.103–0.765	0.045 ${}^{*}$	0.577	0.337–0.988	0.025 ${}^{*}$	11.589	1.353–99.249
ER	0.004 ${}^{*}$	3.082	1.426–6.663	0.016 ${}^{*}$	1.843	1.121–3.029

Cox regression and Logistic regression analysis were used to differentiate the prognostic markers, HR $=$ hazard ratio, OR $=$ odds ratio, CI $=$ confidence interval. ${}^{*}$ Significance defined by $p<$ 0.05.

Survival curves (PFS and OS) according to CD133 tumor biomarker are demonstrated in Fig. 2.

3.5 Predictors of response and survival in univariate and multivariate analyses

3.5.1 Response rate

In multivariate analysis, CD133 (OR $=$ 11.589, % CI, 1.353–99.249; $P=$ 0.025) was the only independent variable and predictor of response rate, whereas other factors (TNBC, ER, PR, Tumor size) lost their statistical significance as predictors of response rate in multivariate analyses.

3.5.2 PFS

ER (HR $=$ 1.843, 95% CI, 1.121–3.029; $P=$ 0.016) was the most influential independent predictor of EFS in multivariate analyses, followed by CD133 expression (HR $=$ 0.577, 95% CI, 0.337–0.988; $P=$ 0.045), which was demonstrated after controlling other variables in multivariate analyses; TNBC also lost its significance in the multivariate model.

3.5.3 OS

In the multivariable analysis, ER (HR $=$ 3.082, 95% CI, 1.426–6.663; $P=$ 0.004) and CD133 expression (HR $=$ 0.281, 95% CI, 0.103–0.765; $P=$ 0.013) stood as the most powerful predictors of the OS. Univariate and multivariate analyses of 100 patients with BC according to clinic-pathological variables are displayed in Table 4.

4. Discussion

CD133 is the most extensively used cell surface marker for isolating and identifying CSCs in a range of hematological and solid tumors, including BC [25, 26, 27]. CSCs are thought to be a source of tumor progression and cause tumor growth and spread [28].

Our study found a statistically significant correlation between CD133 positive cases and some adverse clinicopathological features such as high grade, higher tumor, and nodal TNM staging, indicating that this marker is associated with tumors with more aggressive characteristics. Surprisingly, the study found a positive correlation between CD133 and lung metastases. This finding can be attributed to the fact that CD133 induces tumor metastases via epithelial-mesenchymal transition (EMT) with the acquisition of mesenchymal tumor characteristics, with the lung being the most common site of distant metastases in metastatic mesenchymal tumors, or maybe selection bias as the study recruited only those with stage IV breast cancer. There was no correlation with other variables like ER, PR, Her2, age, premenopausal status, and TNBC cases.

These results are in agreement with other studies which have revealed a correlation between CD133 and TNM, grade, and lymph node metastases [17, 29, 30, 31]. The present study demonstrated no significant association with patient age, menstruation status, and CD133 expression, which is consistent with Liu et al. work [31]. In contrast to our findings, some studies have demonstrated a correlation with age [20, 32], hormonal receptors [32], and TNBC [33, 34].

As regards hormonal status, the present study revealed no significant association between ER, PR, HER2, and TNBC, which is incompatible with the findings of Joseph et al. who found a significant association between high CD133 expression and ER/PR negativity and HER2 positivity [24]. The discrepancy between the two studies is attributed to different study groups as the current study focused on stage IV BC and different techniques in CD133 evaluation.

The present study showed no significant relationship between the CD133 expression and the histological subtype of BC, which aligns with the study done by Liu et al. [35]. On the contrary, the research conducted by Joseph et al. detected a significant relation of CD133 with histological subtypes. This result may be due to the scarcity of particular subtypes in our study [24].

The current work found no statistical significance with tumor-infiltrating lymphocytes; this may be attributed to the small sample size, and few cases showed dense TILs.

There was a statistically shorter survival time for CD133 positive expression cases than those with CD133 negative expression. Consequently, the CD133 in the current study has emerged as an exclusive and strong independent variable for PFS and OS in the multivariate analysis. These results are consistent with the study by Han et al., which has shown poor survival time with those with CD133 positive expression [17]. In addition, other previous reports have confirmed and are in line with these findings [29, 30]. However, other studies have found no correlation between expression level and the survival outcome [32].

CD133 positive patients had a lower response rate than negative subjects regarding the response rate. In the multivariate analysis, CD133 was elucidated as the only independent predictive marker for the response rate.

As a marker of CSC, CD133 shared the same functions and characteristics with intrinsic resistance and poor response to chemotherapy as a result of slow division, initiation, and maintenance of malignant cells. Several studies have demonstrated a correlation between CD 133 expression and chemoresistance in solid tumors, including the brain [36], colorectal [37], ovarian [38], and Ewing sarcoma [39].

There was a paucity of studies evaluating the response rate related to CD133 expression in breast cancer in general and in metastatic settings. A study done by Nadal et al. on non-metastatic breast cancer cases has discovered chemotherapy resistance in CD133 positive patients [40]. Achuthan et al. also reported similar results in breast cancer cell lines in vitro [41].

To our knowledge, this is the first study to investigate only stage IV breast cancer patients and assess the response rate in correlation with CD133 expression in this subset of patients. The limitations in our study are the small sample size, a short period of follow-up, and study patients received different lines of systemic treatment, including chemotherapy with various protocols and different sequences, which could have affected the results of CD133 as an independent prognostic factor and prognosis may be related to the type of therapy regimen. More large multicenter prospective studies are needed to validate our results in metastatic breast cancer in terms of chemotherapy resistance and survival outcomes.

5. Conclusion

Positive CD133 is correlated with poor prognosis in metastatic breast cancer patients.

Footnotes

Acknowledgments

None.

Author contributions

Conception: AM Hefni and AM Sayed.

Lab work: MT Hussien.

Interpretation or analysis of data: AM Hefni, MT Hussien.

Preparation of the manuscript: AM Hefni, AG Gabr and AM Sayed, MT Hussien and AZ Abdalla.

Revision for important intellectual content: AM Hefni, AZ Abdalla, MT Hussien and AM Sayed.

Supervision: AM Hefni, MT Hussien, AG Gabr and AZ Abdalla.

References

Ascha

M.S.

Ostrom

Q.T.

Wright

Kumthekar

Bordeaux

J.S.

Sloan

A.E.

Schumacher

F.R.

Kruchko

and Barnholtz-Sloan

J.S.

, Lifetime occurrence of brain metastases arising from lung, breast, and skin cancers in the elderly: A SEER-Medicare study, Cancer Epidemiol. Prev. Biomarkers. 28 (2019), 917–925.

Gewaifel

Bahnasy

Kharboush

and Elsharkawy

, Geospatial analysis of breast cancer in Alexandria: Application of a novel public health tool, Egypt. J. Community Med. 37 (2019), 27–36.

van Oosterom

A.T.

, Docetaxel (Taxotere): an effective agent in the management of second-line breast cancer, in: Semin. Oncol., 22 (1995), 22–28.

Largillier

Ferrero

J.-M.

Doyen

Barriere

Namer

Mari

Courdi

Hannoun-Levi

J.M.

Ettore

Birtwisle-Peyrottes

Balu-Maestro

Marcy

P.Y.

Raoust

Lallement

and Chamorey

, Prognostic factors in 1,038 women with metastatic breast cancer, Ann. Oncol. Off. J. Eur. Soc. Med. Oncol. 19 (2008), 2012–2019.

Chen

Liu

Yang

Liu

You

Dong

, and Lyu

, Development and validation of a nomogram for predicting survival in male patients with breast cancer, Front. Oncol. 9 (2019), 361.

Miraglia

Godfrey

Yin

A.H.

Atkins

Warnke

Holden

J.T.

Bray

R.A.

Waller

E.K.

and Buck

D.W.

, A novel five-transmembrane hematopoietic stem cell antigen: Isolation, characterization, and molecular cloning, Blood. 90 (1997), 5013–5021.

Brugnoli

Grassilli

Al-Qassab

Capitani

and Bertagnolo

, CD133 in breast cancer cells: More than a stem cell marker, J. Oncol. 2019 (2019).

Tirino

Camerlingo

Franco

Malanga

La Rocca

Viglietto

Rocco

and Pirozzi

, The role of CD133 in the identification and characterisation of tumour-initiating cells in non-small-cell lung cancer, Eur. J. Cardio-Thoracic Surg. 36 (2009), 446–453.

Mitra

Banerjee

Misra

Singh

R.K.

Roy

Sengupta

Panda

C.K.

and Roychoudhury

, Interplay between human papilloma virus infection and p53 gene alterations in head and neck squamous cell carcinoma of an Indian patient population, J. Clin. Pathol. 60 (2007), 1040–1047.

10.

Ricci-Vitiani

Lombardi

D.G.

Pilozzi

Biffoni

Todaro

Peschle

and De Maria

, Identification and expansion of human colon-cancer-initiating cells, Nature. 445 (2007), 111–115.

11.

Hermann

P.C.

Huber

S.L.

Herrler

Aicher

Ellwart

J.W.

Guba

Bruns

C.J.

and Heeschen

, Distinct populations of cancer stem cells determine tumor growth and metastatic activity in human pancreatic cancer, Cell Stem Cell. 1 (2007), 313–323.

12.

O’Brien

C.A.

Pollett

Gallinger

and Dick

J.E.

, A human colon cancer cell capable of initiating tumour growth in immunodeficient mice, Nature. 445 (2007), 106–110.

13.

Neuzil

Stantic

Zobalova

Chladova

Wang

Prochazka

Dong

Andera

and Ralph

S.J.

, Tumour-initiating cells vs. cancer ‘stem’cells and CD133: What’s in the name? Biochem. Biophys. Res. Commun. 355 (2007), 855–859.

14.

Tang

Ang

B.T.

and Pervaiz

, Cancer stem cell: Target for anti-cancer therapy, FASEB J. 21 (2007), 3777–3785.

15.

Tume

Paco

Ubidia-Incio

and Moya

, CD133 in breast cancer cells and in breast cancer stem cells as another target for immunotherapy, Gac. Mex. Oncol. 15 (2016), 22–30.

16.

Collina

Di Bonito

Li Bergolis

De Laurentiis

Vitagliano

Cerrone

Nuzzo

Cantile

and Botti

, Prognostic value of cancer stem cells markers in triple-negative breast cancer, Biomed Res. Int. 2015 (2015).

17.

Han

Chen

Zheng

Cheng

Gong

and Wang

, Clinicopathological significance of CD133 and CD44 expression in infiltrating ductal carcinoma and their relationship to angiogenesis, World J. Surg. Oncol. 13 (2015), 1–8.

18.

Kim

S.J.

Kim

Y.S.

Jang

E.D.

Seo

K.J.

and Kim

J.S.

, Prognostic impact and clinicopathological correlation of CD133 and ALDH1 expression in invasive breast cancer, J. Breast Cancer. 18 (2015), 347–355.

19.

Lin

C.-H.

Liu

C.-H.

Wen

C.-H.

P.-L.

and Chai

C.-Y.

, Differential CD133 expression distinguishes malignant from benign papillary lesions of the breast, Virchows Arch. 466 (2015), 177–184.

20.

Mansour

S.F.

and Atwa

M.M.

, Clinicopathological significance of CD133 and ALDH1 cancer stem cell marker expression in invasive ductal breast carcinoma, Asian Pacific J. Cancer Prev. 16 (2015), 7491–7496.

21.

Kalli

Semine

Cohen

Naber

S.P.

Makim

S.S.

and Bahl

, American joint committee on cancer’s staging system for breast cancer, eighth edition: What the radiologist needs to know, RadioGraphics. 38 (2018), 1921–1933.

22.

Eisenhauer

E.A.

Therasse

Bogaerts

Schwartz

L.H.

Sargent

Ford

Dancey

Arbuck

Gwyther

Mooney

Rubinstein

Shankar

Dodd

Kaplan

Lacombe

and Verweij

, New response evaluation criteria in solid tumours: Revised RECIST guideline (version 1.1), Eur. J. Cancer. 45 (2009), 228–247.

23.

Salgado

Denkert

Demaria

Sirtaine

Klauschen

Pruneri

Wienert

Van den Eynden

Baehner

F.L.

and Pénault-Llorca

, The evaluation of tumor-infiltrating lymphocytes (TILs) in breast cancer: Recommendations by an International TILs Working Group 2014, Ann. Oncol. 26 (2015), 259–271.

24.

Joseph

Arshad

Kurozomi

Althobiti

Miligy

I.M.

Al-Izzi

Toss

M.S.

Goh

F.Q.

Johnston

S.J.

and Martin

S.G.

, Overexpression of the cancer stem cell marker CD133 confers a poor prognosis in invasive breast cancer, Breast Cancer Res. Treat. 174 (2019), 387–399.

25.

Villagrasa

Álvarez

P.J.

Osuna

Garrido

J.M.

Aránega

and Rodríguez-Serrano

, Exosomes derived from breast cancer cells, small Trojan horses? J. Mammary Gland Biol. Neoplasia. 19 (2014), 303–313.

26.

Wang

X.-Y.

Penalva

L.O.F.

Yuan

Linnoila

R.I.

Okano

and Glazer

R.I.

, Musashi1 regulates breast tumor cell proliferation and is a prognostic indicator of poor survival, Mol. Cancer. 9 (2010), 1–12.

27.

Bühring

Seiffert

Bock

T.A.

Scheding

Thiel

Scheffold

Kanz

and Brugger

, Expression of novel surface antigens on early hematopoietic cells a, Ann. N. Y. Acad. Sci. 872 (1999), 25–39.

28.

Kummermehr

J.C.

, Tumour stem cells&the evidence and the ambiguity, Acta Oncol. (Madr). 40 (2001), 981–988.

29.

Martin

T.A.

and Jiang

W.G.

, Evaluation of the expression of stem cell markers in human breast cancer reveals a correlation with clinical progression and metastatic disease in ductal carcinoma, Oncol. Rep. 31 (2014), 262–272.

30.

Zhao

and Lu

, Aberrant expression of CD133 protein correlates with Ki-67 expression and is a prognostic marker in gastric adenocarcinoma, BMC Cancer. 10 (2010), 1–6.

31.

Liu

Zheng

Jin

and Dong

, Expression of CD133, PAX2, ESA, and GPR30 in invasive ductal breast carcinomas, Chin. Med. J. (Engl). 122 (2009).

32.

Currie

M.J.

Beardsley

B.E.

Harris

G.C.

Gunningham

S.P.

Dachs

G.U.

Dijkstra

Morrin

H.R.

Wells

J.E.

and Robinson

B.A.

, Immunohistochemical analysis of cancer stem cell markers in invasive breast carcinoma and associated ductal carcinoma in situ: Relationships with markers of tumor hypoxia and microvascularity, Hum. Pathol. 44 (2013), 402–411.

33.

Liu

T.J.

Sun

B.C.

Zhao

X.L.

Zhao

X.M.

Sun

Yao

Dong

X.Y.

Zhao

and Liu

, CD133+ cells with cancer stem cell characteristics associates with vasculogenic mimicry in triple-negative breast cancer, Oncogene. 32 (2013), 544–553.

34.

Xia

, CD133 mRNA may be a suitable prognostic marker for human breast cancer, Stem Cell Investig. 4 (2017).

35.

Liu

T.T.

X.F.

Wang

and Yang

J.L.

, CD133 expression and clinicopathologic significance in benign and malignant breast lesions, Cancer Biomarkers. 28 (2020), 293–299.

36.

Liu

Yuan

Zeng

Tunici

Abdulkadir

I.R.

Irvin

Black

K.L.

and Yu

J.S.

, Analysis of gene expression and chemoresistance of CD133+ cancer stem cells in glioblastoma, Mol. Cancer. 5 (2006), 1–12.

37.

Ong

C.W.

Kim

L.G.

Kong

H.H.

Low

L.Y.

Iacopetta

Soong

and Salto-Tellez

, CD133 expression predicts for non-response to chemotherapy in colorectal cancer, Mod. Pathol. 23 (2010), 450–457.

38.

Baba

Convery

P.A.

Matsumura

Whitaker

R.S.

Kondoh

Perry

Huang

Bentley

R.C.

Mori

and Fujii

, Epigenetic regulation of CD133 and tumorigenicity of CD133+ ovarian cancer cells, Oncogene. 28 (2009), 209–218.

39.

Jiang

Gwye

Russell

Cao

Douglas

Hung

Kovar

Triche

T.J.

and Lawlor

E.R.

, CD133 expression in chemo-resistant Ewing sarcoma cells, BMC Cancer. 10 (2010), 1–12.

40.

Nadal

Ortega

F.G.

Salido

Lorente

J.A.

Rodríguez-Rivera

Delgado-Rodríguez

Macià

Fernández

Corominas

J.M.

García-Puche

J.L.

Sánchez-Rovira

Solé

and Serrano

M.J.

, CD133 expression in circulating tumor cells from breast cancer patients: Potential role in resistance to chemotherapy, Int. J. Cancer. 133 (2013), 2398–2407.

41.

Achuthan

Santhoshkumar

T.R.

Prabhakar

Nair

S.A.

and Pillai

M.R.

, Drug-induced senescence generates chemoresistant stemlike cells with low reactive oxygen species, J. Biol. Chem. 286 (2011), 37813–37829.

CD133 is an independent predictive and prognostic marker in metastatic breast cancer

Abstract

BACKGROUND:

OBJECTIVES:

METHODS:

RESULTS:

CONCLUSIONS:

Keywords

1. Introduction

2. Materials and methods

2.1 Study design and patients

2.2 Assessment of breast cancer status

2.3 CD133 immunohistochemistry

2.4 Evaluation of CD133 expression

2.5 Statistical analysis

3. Results

3.1 Demographics and clinicopathological characteristics

Table 1 Demographic, clinical and pathological characteristics according to CD133 tumor marker ( n = 100)

Table 2 The association between the CD133 tumor marker and the best overall response rate (BOR) of studied participants for the different lines of treatment ( n = 99)

3.5.1 Response rate

3.5.2 PFS

3.5.3 OS

4. Discussion

5. Conclusion

Footnotes

Acknowledgments

Author contributions

References

Table 1
Demographic, clinical and pathological characteristics according to CD133 tumor marker ( $n=$ 100)

Table 2
The association between the CD133 tumor marker and the best overall response rate (BOR) of studied participants for the different lines of treatment ( $n=$ 99)