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Abstract
Aim: This study aims to investigate potential associations between the stem cell population and the degree of tumor regression in breast carcinomas treated with neoadjuvant therapy. Settings and Design: The study included 92 patients with breast carcinoma who received neoadjuvant therapy. Tumor regression was defined based on Miller and Payne grading system. Patients with grade 1 or 2 regression on a 5-point scale were included in group 1 (n = 37), grade 3 regression in group 2 (n = 32), and grade 4 or 5 regression in group 3 (n = 23). Materials and Methods: Immunohistochemical staining was performed on paraffin block sections of every case using CD44, CD24, CD29, CD133, ID4, and ALDH1 antibodies to detect stem cells. Statistical Analysis Used: IBM Statistical Package for the Social Sciences (SPSS), version 23.0 (IBM Corp., Armonk, NY, USA) software was used for statistical analyses, and a P value less than 0.05 was considered statistically significant. Results: Histologically high-grade tumors are more common in the near-complete/complete response group (P = 0.004). HER2-positive tumors were more common in the complete/near-complete response group (P = 0.054). Tumor cells positive for stem cell markers CD44 and CD24 were more common in the poor response group (P = 0.027 and P = 0.001, respectively). CD29 expression was reduced in the posttreatment residual tumor tissue in the near-complete/complete response group. Conclusion: High CD44 and CD24 expression may be a predictor of poor response/nonresponse to neoadjuvant therapy in breast carcinomas.
Background: In recent years, stem cells have been defined as the main cell population responsible for resistance to anticancer therapies.
Keywords: Breast Cancers, neoadjuvant treatment, stem cell markers, stem cells, tumor regression grade
How to cite this article: Introduction 
Tumoral tissues show cellular and molecular heterogeneity. A group of undifferentiated tumor cells harboring stem cell characteristics take part in this heterogeneous tumor tissue.
Tumor cells that have different molecular characteristics within a tumor mass may respond differently to anticancer therapies, or even certain tumor cells may exhibit drug resistance. Advances in the understanding of adult stem cells resulted in the assumption that these cells were essential components responsible for carcinogenesis and metastases. These cells have been referred to as cancer stem cells (CSCs).[1] CSCs can both self-renew and differentiate to a variety of cell types leading to tumor cell diversity. The assumption that CSCs are the unique cell types capable of tumorigenesis has been widely accepted.[2],[3],[4],[5],[6],[7]
Heterogeneity in normal tissues and heterogeneity in tumor tissues are comparable.[2] The normal development process allows progenitor cells to differentiate, and cellular plasticity allows these cells to adapt to changing environments.
A dynamic balance exists between stem cells and differentiated cells during normal turnover. The balance may be disturbed by transcriptional, epigenetic, or environmental alterations, and consequently, differentiated cells undergo a dedifferentiation process and acquire stem cell characteristics, leading to reprogramming toward a tumorigenic outcome.[8]
Currently, cancer is defined as a stem cell disorder. These cells are believed to play a critical role in tumor initiation, tumor progression, immortality, radio-chemotherapy resistance, and recurrence.[9] CSCs have a great ability for adapting their metabolism to the microenvironment and owing to this ability, they may develop adaptive changes or even drug resistance following anticancer treatment. Adaptive modifications in epithelial cancers include epithelial-to-mesenchymal transition (EMT) and the reverse, mesenchymal-to-epithelial transition (MET). These processes (EMT and MET) have been considered developmental programs acquired during tumor progression.[3],[6] These two processes describe the ability of the cells that is referred to as cellular plasticity and allow reciprocal transitions between mesenchymal and epithelial states.Tumor cells arriving distant sites by acquiring the ability to invade through EMT can regain epithelial traits through MET at these distant sites.[6],[10],[11]
Tumorigenesis is similar to the wound healing process as mesenchymal stem cells (MSC) recruit to the event site in both conditions. MSC ensures tissue regeneration by differentiating to various epithelial cell lines. Many cancers may originate from this condition defined as MET, and this may explain the association between chronic irritation and carcinogenesis.[10],[11]
The EMT process is controlled by various cytokines and growth factors such as transforming growth factor beta (TGF-β). TGF-β activity is impaired in tumors. Therefore, cancer cells acquire the ability to invade and metastasize through EMT induction.[3],[6]
CSC markers have been previously investigated in leukemias, and leukemia stem cells have been defined as having CD34+/CD38– cells. Furthermore, stem cells in brain, colon, and pancreatic cancers were CD133+ cells; initially, stem cells in breast cancers were defined as CD44+/CD24 − cells, and these cells were followed by ALDH1+, ID4+, and ESA+ stem cells.[3],[12],[13]
Neoadjuvant chemotherapy (NAC), defined as systemic chemotherapy administered before surgical removal of the tumor, emerged toward the end of the 20th century as a new therapeutic strategy aimed to reduce the dimensions of the tumor in breast cancer. Currently, neoadjuvant therapies have the following four goals: to render the tumor operable, to allow conservative surgery, to improve overall survival, and to assess the clinical response to treatment rapidly and objectively. Increasing experience with NAC has allowed us to predict patients who would respond well or even give a complete response to treatment and which patients would not respond.[14],[15],[16]
Considering that stem cell–rich breast cancers show a lower rate of response to NAC, we investigated if the stem cell status could predict response to treatment in this study. Fort this purpose, various stem cell markers were applied to tumor tissues and their possible associations with response to NAC were investigated. Furthermore, correlations between posttreatment alterations in stem cell status and NAC responses were evaluated.
Materials and Methods The study included patients with breast cancer who received NAC in our university hospital between November 2011 and November 2018. Patients who underwent pretreatment diagnostic biopsy or posttreatment surgery in another health facility were excluded from the study. Data on the age and tumor characteristics were obtained from pathology reports.
Tumor regression was defined based on Miller and Payne grading system. Treatment response was defined based on loss of tumor cells and fibrosis, elastosis, histiocytic infiltration, calcific degeneration that replaced lost cells, and vasculopathic changes.
According to the Miller and Payne grading system, treatment responses are assessed on a 1–5 scale as follows: grade 1: no change or some alteration to individual malignant cells, but no reduction in overall cellularity; grade 2: mild response (up to 30% loss of tumor cells); grade 3: moderate response (30%–90% loss of tumor cells); grade 4: marked response (more than 90% loss of tumor cells); and grade 5: complete response.[17]
In this study, patients with grade 1 or 2 regression accepted as “poor response” were included in group 1 (n = 37), patients with grade 3 regression accepted as “partial/good response” were included in group 2 (n = 32), and patients with grade 4 or 5 regression, that is, those with “near-complete and complete pathological response” were included in group 3 (n = 23).
Patients were grouped based on treatment responses, and the study groups were compared to each other regarding the mean age, tumor diameter before the treatment, histological tumor types, molecular tumor types, histological grade, primary tumor stage (pT), and regional lymph node involvement (pN).
Intergroup comparisons of stem cell markers were conducted both before and after treatment. Finally, the study groups were compared to each other with regard to changes from the baseline to posttreatment assessment in stem cell markers. For these purpose, immunohistochemical staining was performed on paraffin block sections using CD44 (MRQ 13; Cell Marque), CD24 (SN36; Thermo Fisher Scientific), CD29/ITGB 1 (7F10; Thermo Fisher Scientific), CD133 (D4W4N; Cell Signaling Tecnology), ID4 Polyclonal (Thermo Fisher Scientific), and ALDH1 (ALDH1 A1/44; Cell Marque) Mouse monoclonal antibodies to detect stem cells. The procedure was performed with Ventana ultra-automated immunostainer, in compliance with the instructions defined in the respective manual of each antibody. Immunohistochemical staining with stem cell antibodies were detected as nuclear, cytoplasmic, or membranous locations according to manual [Figure 1]. Based on the extensiveness, samples were scored as follows: 0 if staining was present in less than 1% of cells, +1 if staining was present in 1%–10% of cells, +2 if staining was present in 10%–50% of cells, and + 3 if staining was present in >50% of cells. Based on the intensity of staining, samples were scored as follows: none (0), weak (+1), moderate (+2), and strong (+3). The total score was calculated by summing up the extensiveness score and intensity score and ranged from 0 to 6. Samples were classified based on total scores: negative if the total score was 0, weak positive if the total score was 1 or 2, moderate positive if the total score was 3 or 4, and strong positive if the total score was 5 or 6.
Figure 1: Different staining patterns and intensities with various stem cell markersNegative or weak positivity (total score 0–2) was categorized as category 0 and moderate and strong positivity (total score 3–6) were categorized as category 1 and used in the statistical analysis.
Statistical Analysis
Data were summarized as mean ± standard deviation and median (interquartile range [IQR]) for continuous variables and frequencies (percentiles) for categorical variables. One-way analysis of variance (ANOVA) test or Kruskal Wallis test was used for comparing more than two independent groups, depending on the distributional properties of the data. Chi-square test was used for proportions and its counterpart Fisher's exact test was used when the data were sparse. McNemar test was used to determine if the distribution of categorical data changed with time. All analyses were performed using Statistical Package for the Social Sciences (SPSS), version 23.0 (IBM Corp., Armonk, NY, USA). A P value < 0.05 was considered as statistically significant.
Results The study included 92 patients with breast cancer who received NAC between 2011 and 2018. Treatment responses were assessed according to the Payne and Miller regression grading system; the response was weak in 37 patients (group 1), partial/good in 32 patients (group 2), and near-complete/complete pathological response in 23 patients (group 3). No difference was found between age and tumor size comparisons between the groups (P = 0.328 and P = 0.499, respectively). The distribution of categorical variables by treatment response groups is presented in [Table 1]. Based on [Table 1], the rate of histological type 3 tumors was higher in the poor response group compared to other two response groups (P = 0.036).
Table 1: Distribution of categorical variables by treatment response groupsWhen the tumors were divided into four functional groups, the rate of HER2 + tumors was higher in the complete/near-complete response group compared to other response categories and the difference was considered almost significant (P = 0.054). This difference may be expected to reach the significance level as the sample size increases.
Tumors were also stratified into luminal (luminal A + B) and non-luminal (HER2 + and triple-negative [TN]) groups, and although non-luminal tumors were more common in the complete response group (43.5%), the difference was not statistically significant (P = 0.144).
When the tumors were stratified into three histological grades, significant differences were found between grade 2 and grade 3 in responses (P = 0.025). Grade 2 tumors are more common in the partial/good response, whereas grade 3 tumors are more common in the complete/near-complete response group.
Tumors were also stratified into histological low-grade and high-grade groups, and a significant difference was found in the distribution of these two groups by response categories. The rates of high-grade tumors were more common in the complete/near-complete response group (P = 0.004).
No significant differences were found among response categories in the primary tumor stage (pT) and regional lymph node (pN) involvement (P = 0.507 and P = 0.699, respectively).
Pre-NAC stem cell markers identified in tru-cut biopsy specimens by NAC response categories are shown in [Table 2]. Based on these results, CD44 and CD24 expressions were common in the poor response category, whereas CD44 and CD24 expressions were less common in the near-complete/complete response category (P = 0.027 and P = 0.001, respectively). ALDH1 expression was higher in the partial response group compared to the other two groups (P = 0.019), and CD29 expression was higher in the near-complete/complete response group compared to the other two groups (P < 0.001). No intergroup differences were found in ID4 and CD133 expressions.
Table 2: Intergroup comparisons of binary (two-response) stem cell markers before NACPost-NAC distribution of stem cell markers in resection materials by response categories is shown in [Table 3]. According to these results, no significant intergroup differences were found in stem cell distributions after NAC (P > 0.05).
The McNemar test was used to compare two-response categorical variables for changes in stem cell marker expression status from baseline to post-NAC and the results are shown in [Table 4]. According to these results, only post-NAC CD29 expression reduced significantly from baseline in the near-complete/complete response group (P = 0.016).
Table 4: Intragroup comparisons of pre-NAC and post-NAC stem cell markers (positive/negative) Discussion The outcomes with NAC are promising in breast cancers. NAC can reduce tumor size in 95% of the breast cancers, whereas tumor stage regression can be observed in 62.7% of all cases and a pathological complete response (pCR) can be achieved in 20%–30% of all cases. Higher 10-year survival rates have been reported in patients who achieved >50% tumor reduction.[18] Payne and Miller regression grade was found to be an independent predictor of metastasis-free survival and residual breast tumor.[19] and lymph node status was found to be an independent predictor of local recurrence-free survival following treatment.[19]
In this study, breast cancers were divided into four functional subtypes, including luminal A (n = 32; 34.8%), luminal B (n = 33; 35.9%), HER2+ (n = 15; 16.3%), and TN (n = 12; 13.0%) subtypes, with a distribution in line with that reported in the literature.[20]
Higher pCR rates have been reported in HER2-positive and TN tumors.[14],[19] A lower risk for recurrences has been reported in patients achieving pCR compared to those who have achieved a partial response.[14]
In this study, the rate of HER2 + tumors was almost significantly higher in the complete/near complete response group (39.1%) compared to other tumor subtypes (P = 0.054). When tumors were further stratified as luminal and non-luminal, the rate of non-luminal tumors (HER2+ and TN) was 43.5% in the complete/near-complete response group compared to the other two response groups. Although the difference was not statically significant (P = 0.144), more favorable responses can be estimated in larger study samples.
High tumor grade, progesterone receptor (PR) (−) status, and treatment with trastuzumab were found to be predictors of pCR, but no association was found between pCR and luminal-type tumors.[21]
Breast cancers are composed of cell populations that exhibit a hierarchical organization with stem cells on the top of the hierarchical structure. CSCs both mediate the development of metastases and contribute to drug resistance. CSCs have cellular plasticity. This trait allows reciprocal transition between mesenchymal and epithelial states, promoting metastasis.
There are associations between CSCs and clinical response. Higher percentages of stem cells in tumors have been associated with poor prognosis and poor survival.[20] It is suggested that radio-chemotherapy may not be effective enough to eliminate CSCs, whereas CSCs mediate recurrences and distant metastasis following radio-chemotherapy.[2],[10],[22],[23],[24],[25] Therefore, CSCs should be effectively targeted to provide cure.
CSCs are regulated by the tumor microenvironment. Immunotherapeutic approaches may be appropriate to target stem cells.[26],[27]
Invasion and metastasis are not random events and consist of an organized pathophysiological process involving several organ-specific, multi-stage interactions between the cancer cell and the host cell. A study reported that the activation of invasive CSCs was mediated by CXCR4 receptors, and immunophenotypic analysis showed that invasive CD133+/CXCR4 CSCs were responsible for pancreatic cancer metastasis.[28]
Recently, new treatment strategies have been developed using chimeric antigen receptor-modified T (CART) cells in several tumors.[22] CART cells specifically target tumor cells using their chimeric antigen receptors (CARs), providing antitumor treatment by activating and proliferating T cells. CART cells are able to specifically recognize many potential antigens including CD44, CD90, CD133, ALDH, and EpCAM, which are expressed on the stem cell surface. An effective cure can be provided by targeting stem cells using CART cells in the treatment of cancer.[22]
Pharmaceuticals selectively targeting stem cells are promising particularly when these are combined with chemotherapy. Low-dose metformin, a standard of care therapy in diabetes mellitus, can both prevent cellular transformation and selectively eliminate CSCs. Combination treatment with metformin and doxorubicin has been demonstrated to kill both stem cells and non-stem cells in cell cultures, suggesting that this combination therapy might provide a reduction in tumor size and prevent relapses.[23]
Cancer cell plasticity and the conversion of non-stem cells into stem cells may also occur after the treatment.[27] In breast cancers that do not contain CSCs, cancer cells can be reprogrammed as stem cells when they are exposed to ionizing radiation. Cisplatin therapy was found to be able to induce CSC self-renewal and survival by increasing BMI1 expression in squamous cell carcinomas of the head and neck. Cisplatin and vemurafenib used for the treatment of melanoma can enrich the slow-cycling cell population, leading to tumor growth in the long term. Colon cancer cells can acquire MSC like traits following treatment with 5-Fluorouracil (5-FU).
Anti-HER2 therapy may eliminate CSCs and EMT. pCR was achieved in 44% of 18 patients with HER2 (+) breast cancer who had received neoadjuvant therapy including trastuzumab, lapatinib, and paclitaxel. Also, 62% of patients who achieved a complete response had been CD44 (+) and became negative after the treatment; however, CD44 positivity persisted or the rate of CD44 (+) cells was increased in patients who could not achieve complete regression, while no statistically significant association was found between other biomarkers and pCR. Any increase in the rate of CD44 (+) cells after dual anti-HER2 therapy may predict a poor response to adjuvant anti-HER2 therapy and chemotherapy (CT).[29]
In the present study, the rate of CD44 (+) cells had been 52.2% in the tumor group with complete/near-complete response before NAC and was increased to 83.3% after treatment. Any change to this extent was not observed in the other two groups. A possible interpretation of this observation was that CD44 + CSCs were the principal cells resistant to therapy and the rate of this cell population was increased in the residual tumor.
Chemotherapy-resistant CSCs are the main barrier to effective treatment. CSC-targeted EpCAM aptamers combined with doxorubicin (Apt-DOX) have been investigated to overcome the CT resistance of CSCs. Doxorubicin was retained in nuclei for a long term, and its effectiveness in preventing tumor growth was more than three times greater if colorectal cancer cells were incubated with doxorubicin. The survival was improved, and 30 times lower stem cell densities were detected. The study concluded that CSC-targeted EpCAM aptamers could overcome CT resistance.[30]
Results from this study indicate that pre-NAC determination of CD44+ cell status is particularly important as it can predict treatment response. Poor treatment response can be predicted in CD44-rich tumors. CD29 expression was found to be high (65.2%) before NAC, whereas it was found to be low after NAC in the complete/near-complete response group (P = 0.016). CD29 (integrin beta1) is involved in cell–extracellular matrix component binding that prevents cell death and proliferation of tumor cells. Furthermore, the binding results in recurrences and metastasis, whereas deadhesion leads to apoptosis. The reduction in the CD29 expression in residual tumor tissues after NAC in the near-complete group may indicate a lower risk for recurrences and metastasis.
In conclusion, neoadjuvant or adjuvant treatment strategies should directly target stem cells as these cells are resistant to treatment and their formation can be induced by radio-chemotherapy. Being informed about the stem cell status is important in terms of treatment options if recurrences or metastasis occur during the course of the disease.
Acknowledgement
This study has been supported by Coordinatorship of Scientific Research Projects of Bezmialem Vakif University Rectorate as project number 3.2016/30.
Financial support and sponsorship
Nil.
Conflicts of interest
There are no conflicts of interest.
References 
Source of Support: None, Conflict of Interest: None
CheckDOI: 10.4103/ijpm.ijpm_1274_21
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