Response prediction to oxaliplatin plus 5‑fluorouracil chemotherapy in patients with colorectal cancer using a four‑protein immunohistochemical model
- Authors:
- Published online on: June 14, 2019 https://doi.org/10.3892/ol.2019.10474
- Pages: 2091-2101
Abstract
Introduction
Colorectal cancer (CRC) is the third most commonly diagnosed cancer and the leading cause of cancer-associated mortality worldwide (1). Traditional chemotherapy remains one of the standard treatments of CRC. In particular, oxaliplatin plus 5-fluorouracil (5-FU)-based regimens, also known as FOLFOX, XELOX and SOX, are the first-line chemotherapy regimens for patients with advanced CRC (aCRC); however, the objective response rate of this regimen is estimated to be ~50% (2). In addition, numerous patients with CRC cannot benefit from chemotherapy due to intrinsic or acquired chemotherapy resistance (3). It is therefore difficult for oncologists to select the most suitable regimens for patients with CRC. Determining effective and distinct biomarkers to characterize patients who are intrinsically resistant to oxaliplatin plus 5-FU regimens would have a significant influence in clinical practice.
Several tumor molecular markers that can predict the efficacy of 5-FU or oxaliplatin have been reported, including DNA damage repair system genes, auch as X-ray repair cross complementing 1 (4), ERCC excision repair 1, endonuclease non-catalytic subunit (5) and ERCC excision repair 5 endonuclease (6), certain key enzymes in the chemotherapy-associated signaling pathway, such as thymidylate synthase (7), and other molecules, such as NKx2-3 and transforming growth factor-β1-induced transcript 1 (8), homeobox B8 and kallikrein related-peptidase 11 (9). However, the predictive value of a single molecular marker is limited due to the participation of multiple genes and proteins, rather than a single gene or protein, in the process of drug resistance. It is therefore crucial to detect multiple genes and proteins, and establish a predictive model that could determine the efficacy and accuracy of the oxaliplatin plus 5-FU regimen in patients with aCRC rather than using the predictive value of one molecular marker only (10).
Previous studies reported that carcinoma-associated fibroblasts (CAFs) are a pivotal part of the tumor microenvironment and serve a crucial role in tumor drug resistance. Gonçalves-Ribeiro et al (11) reported that CAFs have a protective effect on CRC cells and could be associated with chemotherapy resistance in patients with CRC. Furthermore, CAFs can induce the translocation of AKT, survivin and ERK to the nucleus of CRC cells, induce AKT and ERK phosphorylation, and upregulate survivin expression. These phenomena ensure correct DNA repair and accurate cell entrance and exit from mitosis in the presence of chemotherapy. Oxaliplatin plus 5-FU regimen resistance is subsequently induced via the activation of the PI3K/AKT, MAPK and Janus kinase/signal transducer and activator of transcription (JAK-STAT) signaling pathways. In addition, it has been reported that the MAPK (12–14) and PI3K/AKT/mTOR (15–18) pathways serve key roles in drug resistance, notably in CRC, and that PI3K/AKT signaling pathway inhibition can reduce resistance to chemotherapeutic drugs. It was also reported that survivin overexpression, which may be a downstream effect of the MAPK or PI3K-AKT-mTOR signaling pathway (19), is associated with drug resistance in CRC. As all these findings focus on cellular and molecular approaches, it is essential to verify whether they exist in patients with CRC. The present study therefore investigated the association between α-SMA, p-AKT, p-ERK and survivin expression and oxaliplatin plus 5-FU chemotherapy efficacy in patients with aCRC. Since chemotherapy resistance is a multifactorial process, the present study aimed to establish a predictive model that could help oncologists to screen patients with intrinsic resistance to chemotherapeutic drugs.
Materials and methods
Patients and tissue samples
A total of 71 patients diagnosed with aCRC at the Peking Union Medical College Hospital (Beijing, China) between June 2013 and February 2018 were enrolled in the present study. Tissue samples were obtained from patients following radical CRC resection or biopsy during colonoscopy. All patients provided written informed consent. The inclusion criteria were as follows: i) All patients were histologically diagnosed with aCRC and cancer stage was evaluated according to the American Joint Committee on Cancer, 7th edition (20); ii) all patients received oxaliplatin plus 5-FU regimens in accordance with the National Comprehensive Cancer Network guideline (21), including mFOLFOX6, which consisted of 85 mg/m2 oxaliplatin on day 1, 400 mg/m2 leucovorin on day 1, 400 mg/m2 intravenous (IV) bolus 5-FU on day 1, and then 1,200 mg/m2/day for 48 h by continuous IV infusion, repeating every 2 weeks and evaluated every 4 cycles using the Response Evaluation Criteria in Solid Tumors (RECIST 1.1) (22), or XELOX, which consists of 130 mg/m2 IV oxaliplatin on day 1 and 1,000 mg/m2 oral capecitabine twice daily for 14 days, repeating every 3 weeks and evaluated every 2–3 cycles using RECIST; iii) non-responders to chemotherapy (NRCs) included patients with stage IV CRC who received first-line chemotherapy and were identified as having progressive disease when evaluated the first time (after 4 cycles of mFOLFOX6 or 2–3 cycles of XELOX), and patients with stage III CRC who received adjuvant chemotherapy following surgery and who relapsed within 6 months. Chemotherapy responders (CRs) referred to patients with stage IV CRC who received first-line chemotherapy and who were evaluated with partial regression or complete regression at most. The chemotherapy responses were evaluated independently in all patients by two oncologists using RECIST. If their conclusions were different, a third oncologist independently evaluated the case, and the three oncologists consulted and obtained the final result together. Patients with CRC who were identified as stable after chemotherapy or as progressive disease following the first evaluation were excluded from the present study.
Immunohistochemistry (IHC) staining and scoring
IHC staining was performed on 4-µm thick sections of paraffin-embedded tissue samples to detect the protein expression levels of α-SMA, p-AKT, p-ERK and survivin. Briefly, sections were deparaffinized with xylene, rehydrated in decreasing gradient of alcohol and immersed in distilled water for 5 min. Sections were then incubated with EDTA antigen retrieval buffer (pH 8.0) in a microwave for 15 min. Sections were treated with 3% hydrogen peroxide for 25 min at room temperature in the dark to block endogenous peroxidase activity, washed with PBS three times for 5 min and blocked with 3% bovine serum albumin at room temperature for 30 min. Sections were incubated with the following primary antibodies at 4°C overnight: Mouse anti-human α-SMA (cat. no. GB13044, 1:1,000; Servicebio), mouse anti-human p-ERK (cat. no. GB13004; 1:1,000; Servicebio), rabbit anti-human p-AKT (cat. no. GB13012-3; 1:100; Servicebio) and rabbit anti-human survivin (cat. no. 10508-1-AP; 1:100; ProteinTech Group, Inc.). Sections were then incubated with goat anti-mouse secondary antibody (cat. no. GB23301; 1:200; Servicebio; Startech) or goat anti-rabbit secondary antibody (cat. no. GB13044; 1:1,000; Servicebio; Startech) at room temperature for 50 min. Sections were developed using 3,3′-diaminobenzidine and counterstained with hematoxylin at room temperature for 5 min. Images were captured under light microscopy.
All slides were independently evaluated by two pathologists who had no knowledge of the clinical information. If their conclusions were different, a third pathologist independently evaluated the case, and the three pathologists discussed and obtained the final result together. Positive α-SMA expression was localized in the cytoplasm of CAFs; positive survivin expression was localized in the cytoplasm of CRC cells; positive p-AKT and p-ERK expression was localized in the nucleus of CRC cells due to AKT and ERK nuclear translocation. The staining intensity of α-SMA expression was visually scored and classified as follows: 0, no staining; 1, light brown staining; and 2, brown staining. The proportion of cells positively stained for α-SMA was graded as follows: 0, no positively stained CAFs/total CAFs in high-power fields (HPFs); 1, positively stained CAFs/total CAFs in HPFs <10%; 2, positively stained CAFs/total CAFs in HPFs between 10 and 50%; and 3, positively stained CAFs/total CAFs in HPFs >50%. The staining intensity of p-AKT, p-ERK and survivin was visually scored and classified as follows: 0, no staining; 1, light brown staining; and 2, brown staining. The proportion of cells positively stained for p-AKT, p-ERK or survivin was graded as follows: 0, no positively stained CRC cells/total CRC cells in HPFs; 1, positively stained CRC cells/total CRC cells in HPFs <10%; 2, positively stained CRC cells/total CRC cells in HPFs between 10 and 50%; 3, positively stained CRC cells/total CRC cells in HPFs >50%. The staining index (SI) of α-SMA, p-AKT, p-ERK and survivin was calculated as follows: SI = staining intensity × proportion of positive cells. The results were scored from 0 to 6. Low expression was defined as an SI of 0 to 2 and high expression was defined as an SI of 3 to 6 (23).
Follow-up of patient survival
Follow-ups were designed to assess the progression-free survival (PFS), disease-free survival (DFS) and overall survival (OS) of the patients. The latest follow-up of the patients was performed on February 28, 2019.
Data analysis, modeling and validation
Analysis of data characteristics
Expression levels of α-SMA, p-AKT, survivin and p-ERK were used as features for this model and scored as 1 or 2 (low and high expression, respectively). First, the accuracy of these four features as predictive biomarkers for chemotherapy efficiency in patients with aCRC who were identified as NRCs or CRs was analyzed. Subsequently, the two features of highest accuracy were selected, integrated and their accuracy was analyzed. The selection was then increased to three and four features in order to analyze the predictive accuracy.
Probabilistic Neural Networks (PNN) model
An artificial neural network is a type of artificial intelligence; it has been recently widely used in numerous fields, including clinical diagnosis, screening and prognosis prediction (24). As PNNs use the advantages of a radial basis network and classical probability density estimation theory, they offer particularly significant advantages in pattern classification. The PNN classification was therefore used in the present study as the model for classification problem.
The MATLAB Neural Network tool cabinet provides the function ‘newpnn’ to create probabilistic neural networks. Assuming that the input vector is P and the target is T, a two-layer neural network named ‘net’ can be created by using net = newpnn (P, T, SPREAD), where P is a matrix of R feature quantities of the input vector of the Q group, R rows and Q columns, T is a target matrix of S rows and Q columns composed of Q group classification vectors, S is a classification number, and S is 2 if it is a class 2 problem. If the sample of the Jth column belongs to the class i, the element of the Jth column of the ith row of the matrix T is 1, and the remaining columns of the row are ~0. SPREAD is the spreading coefficient of the Gaussian radial basis function. The default value is 0.1. If the SPREAD value is ~0, the created probabilistic neural network can be used as the nearest neighbor classifier.
The MATLAB tool cabinet provides the function y=sim (net, q) by using the probabilistic neural network model created by newpnn, which predicts the target value y of the input vector q using the sim function. This function can therefore be used to verify and check the network model.
The MATLAB tool cabinet also provides the function plotconfusion (y, T), where y is the target output value predicted by the network model and T is the known target value for comparison. In case the target value has only two categories and the function calculates the predictive values as 0 and 1, this function will calculate the number of correct samples and false samples, the accuracy and the error rate.
By using the three functions aforementioned provided by the MATLAB tool cabinet, the classification problem of predicting the target value of 1 (NRCs) or 0 (CRs) based on the four eigenvalues of the provided cases can be accomplished.
The data from the 71 patients were first divided into two parts as follows: 43 patients were used for modeling and the remaining 28 were used for prediction. The data from the 43 patients were divided into two parts by 1, 3, 5 …, 2, 4, 6 …, each of which is 22 lines and 21 lines, and the 22 lines were then further interlaced to obtain two lines of 11, 11 of which were combined with 21 rows into a 32-line dataset (74.4% of the total samples) for modeling, and the remaining 11 patients (25.6% of the total samples) were used for model validation.
Statistical analysis
The clinical and follow-up data were analyzed using SPSS 24.0 (IBM Corp.). The χ2 test and Fisher's exact test were used to assess the association between patient clinical characteristics and the expression of α-SMA, p-AKT, p-ERK and survivin. P<0.05 was considered to indicate a statistically significant difference.
Results
Patient general characteristics
A total of 71 patients with pathologically confirmed aCRC who received oxaliplatin plus 5-FU chemotherapy were enrolled in the present study. Amongst them, 26 patients were defined as NRCs and 45 patients were defined as CRs (Table I). There were 38 men and 33 women, and the median age of these patients was 62 years old. There was no significant difference between pathology type or primary tumor location and the chemotherapy response (P=0.144 and P=0.853, respectively). A flowchart describing the study design is presented in Fig. 1.
 | Table I.Clinical and pathological characteristics of the 71 patients with colorectal cancer who received oxaliplatin plus 5-fluorouracil chemotherapy and were classified as responders (n=46) and non-responders (n=25). |
Association between α-SMA, p-AKT, p-ERK and survivin expression and the chemotherapy response
The expression of α-SMA, p-AKT, p-ERK and survivin was analyzed by IHC in paraffin-embedded specimens from the patients (Fig. 2). The results detailed in Table I demonstrated that the high expression levels of α-SMA, survivin and p-AKT in patients with CRC were associated with oxaliplatin plus 5-FU resistance (NCRs; P<0.001, P<0.001 and P=0.023, respectively). There was no significant difference between p-ERK expression and chemotherapy response (P=0.227).
Association between α-SMA, p-AKT, p-ERK and survivin expression and patient PFS, DFS and OS
Among the 71 patients, there were 61 patients (NRCs:CR ratio, 16:45) with stage IV CRC receiving oxaliplatin and 5-FU as first-line chemotherapy, and 10 patients (NRC:CR ratio, 9:1) with stage III with CRC receiving oxaliplatin and 5-FU as adjuvant chemotherapy. Since stage IV patients with CRC and stage III patients with CRC had different prognoses, the association between PFS, DFS and OS and α-SMA, p-AKT, p-ERK and survivin expression for these patients were separately analyzed using Kaplan-Meier curves.
The results presented in Figs. 3 and 4 demonstrated that in patients with stage IV CRC, the high expression of α-SMA was associated with a significantly reduced PFS time (5.5 vs. 15.0 months; P=0.005; Fig. 3A), and although a shorter OS time was observed (30.7 vs. 35.0 months; P=0.218; Fig. 4A), this difference was not statistically significant. Furthermore, high survivin expression was associated with a significantly reduced PFS time (5.5 vs. 15.0 months; P=0.001; Fig. 3C) and a significantly reduced OS time (15.0 vs. 50.0 months; P<0.001; Fig. 4C). There was no significant association between p-AKT and p-ERK expression and PFS (6.0 vs. 11.0 months and 6.0 vs. 11.0 months; P=0.516 and P=0.466, respectively; Fig. 3B and D) or OS (26.7 vs. 35.0 months and 32.7 vs. 35.0 months; P=0.429 and P=0.446, respectively; Fig. 4B and D) times.
The results presented in Fig. 5 demonstrated that in patients with stage III CRC, there was no significant association between α-SMA, p-AKT, survivin and p-ERK expression and PFS time (6.2 vs. 6.6 months, 5.0 vs. 6.6 months, 6.6 vs. 3.0 months and 5.0 vs. 6.6 months; P=0.949, P=0.158, P=0.427 and P=0.280, respectively; Fig. 5A-D). The results from Fig. 6 also revealed that in patients with stage III CRC, there was no significant association between p-AKT, survivin and p-ERK expression and OS time (15.0 vs. 31.0 months, 24.0 vs. 38.0 months and 12.0 vs. 31.0 months; P=0.207, P=0.264 and P=0.353, respectively; Fig. 6A-C). In patients with stage III CRC, only 1 patient had high α-SMA expression (Fig. 6D) and since this patient was still alive during the study, the association between α-SMA expression and median OS could therefore not be determined.
Predictive model of α-SMA, p-AKT, p-ERK and survivin expression for oxaliplatin plus 5-FU chemotherapy response in patients with aCRC
The error rates of α-SMA, p-AKT, survivin and p-ERK expressions were first analyzed as predictive biomarkers for chemotherapy response in the 43 patients involved in this model. The error rates of individual feature were 28, 35 and 30% for α-SMA, p-AKT and survivin expression, whereas p-ERK expression did not have discriminating ability (Table II) and was not satisfactory for predicting chemotherapy response. Two features with the lowest error rate, α-SMA and survivin expression, were selected from the four features and were used to detect the predictive accuracy of the integrated model. The error rate of the combined model was 26% (Table III). When p-AKT expression was added to this model, the error rate decreased to 21% (Table IV). When p-ERK expression was added to this model, the error rate decreased to 16% and the accuracy was >80% (Table V).
| Table II.Predictive potential of individual features for chemotherapy response in patients with advanced colorectal cancer.a |
| Table III.Predictive potential of the combination of α-SMA expression and survivin expression for chemotherapy response in patients with advanced colorectal cancer.a |
| Table IV.Predictive potential of the combination of α-SMA, survivin and p-AKT expression for chemotherapy response in patients with advanced CRC. |
| Table V.Predictive potential of α-SMA, survivin, p-AKT and p-ERK expression for chemotherapy response in patients with advanced colorectal cancer.a |
Based on these results, the PNN module of the MATLAB tool cabinet was used to establish the model regarding the classification problems. The data from 32 patients were used for modeling, the data from 11 patients were used for validation and the data from 28 patients were used for prediction. The results of modeling, validation and prediction are presented in Table VI. The results demonstrated that the modeling accuracy involving a single feature was ~70%. When the features number increased to 4, the modeling accuracy was 83.7%, the validation accuracy was 92.9% and the prediction accuracy was 85.7%.
| Table VI.Results of modeling, verification and prediction of 4 features for chemotherapy response in patients with advanced colorectal cancer. |
Discussion
The first-line chemotherapy used for patients with aCRC is an oxaliplatin plus 5-FU regimen; however, the response rate is limited. It is therefore necessary to identify biomarkers that could help oncologists to accurately select patients who can benefit from this specific regimen.
As illustrated in the ‘seed and soil’ hypothesis, supportive tumor stroma (the soil) is important for tumor cell (the seed) progression (25,26). CAFs are large and spindle-shaped mesenchymal cells that represent the major components of tumor stroma. Numerous studies have demonstrated that CAFs serve crucial roles in cancer progression and resistance to chemotherapeutic agents (27,28). However, the association between CAFs and traditional chemotherapy in CRC remains unclear. Previous studies reported that cytokines secreted by CAFs, including interleukin (IL)-8, IL-1β, vascular endothelial growth factor, TNF-α, IL-17 and IL-6, can predict a poor response to chemotherapy in CRC cells (29). Gonçalves-Ribeiro et al (11) revealed that CAFs have a protective effect on CRC cells. It has also been reported that the expression of CAFs-associated fibronectin 1 and collagen 3A1 can predict the absence of response to neoadjuvant treatment in locally advanced rectal carcinoma (30). The potential mechanisms involved are as follows: i) CAFs can secrete collagen type I, which can decrease chemotherapeutic drug uptake in tumors and then induce drug resistance (31,32); ii) soluble factors secreted by CAFs can induce the activation of PI3K/AKT/survivin and JAK/STAT pathways, which may provide protection from cell death, ensure correct DNA repair and eventually induce resistance to oxaliplatin and 5-FU; and iii) chemotherapy-treated CAFs can secrete specific cytokines and chemokines, including IL-17A, which may promote cancer-initiating cell self-renewal and tumor resistance (33).
Increasing evidence has demonstrated that chemotherapy resistance may be associated with the activation of two important Ras downstream pathways, the MAPK and the PI3K-AKT-mTOR signaling pathways (12–14). The MAPK pathway regulates numerous physiological processes in cancer cells, including cell proliferation, metastasis and chemoresistance (34). Previous studies revealed that MAPK signaling pathway activation serves key roles in drug resistance, notably in gastric cancer, breast cancer, prostate cancer and CRC. The PI3K/AKT/mTOR pathway also regulates cancer progression and is recognized as a major cause of multidrug resistance in various types of cancer (15–18). This pathway has also been identified as a potent contributor to drug resistance in CRC, particularly resistance to 5-FU and oxaliplatin (35). Inhibition of the PI3K/AKT signaling pathway can reduce insulin-induced chemotherapeutic drug resistance. AKT phosphorylation serves a pivotal part in the activation of the PI3K/AKT/mTOR signaling pathway (36,37).
Survivin is a member of the inhibitor of apoptosis proteins family. Numerous studies have demonstrated that survivin overexpression is associated with drug resistance in different cancer types (19,38–40). The potential mechanisms involved in survivin overexpression-induced drug resistance are as follows: i) Nuclear survivin serves a pivotal role in the formation of spindle fiber assembly via stabilization of microtubule formation leading to cell growth (41); ii) survivin can upregulate the molecular sensor of DNA damage named ku70 and subsequently enhance the repair of DNA double-stranded breaks (42); and iii) survivin can interact with the apoptosis-inducing molecules caspase-3 and casapase-9 and inhibit their apoptotic function (43). Additional evidence has revealed that regulation of survivin expression may be a downstream effect of the MAPK (44–46) or PI3K-AKT-mTOR signaling pathways (45) in different types of cancer.
Based on this evidence, the present study established a predictive model of α-SMA, p-AKT, p-ERK and survivin expression in patients with CRC in order to anticipate the potential intrinsic resistance to the oxaliplatin plus 5-FU regimen. The results from this study demonstrated that α-SMA and survivin overexpression in patients with CRC was significantly associated with oxaliplatin plus 5-FU resistance, which was not the case for p-AKT and p-ERK overexpression; this result was in agreement with previous studies and hypotheses (19,43–45). From these different patterns, the model comprising α-SMA, p-AKT, p-ERK and survivin overexpression was the best predictive pattern. The predictive model of α-SMA, p-AKT, p-ERK and survivin overexpression was of great efficacy (81.3%) and accuracy (81.8%), and could aid oncologists in determining whether an individual could benefit from the oxaliplatin plus 5-FU chemotherapy regimen.
In conclusion, the multifactorial predictive biomarker model of α-SMA, p-AKT, p-ERK and survivin expression in patients with CRC used to predict intrinsic resistance to the oxaliplatin plus 5-FU regimen in the present study was efficient and accurate. These results suggested that patients with high expression of this predictive model may be intrinsically resistant to an oxaliplatin plus 5-FU regimen. This predictive model may be of great help to screen cancer patients who have intrinsic chemoresistance and to prescribe them personalized treatments.
Acknowledgements
Not applicable.
Funding
This study was supported by the National Natural Science Foundation of China (grant nos. 81472785 and 61435001) and the CA MS Innovation Fund for Medical Sciences (grant no. 2016-12M-1-001).
Availability of data and materials
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
Authors' contributions
JG made substantial contributions to conception and design, acquisition of data, analysis and interpretation of data and drafting of the manuscript. ZL made substantial contributions to acquisition of data. JZ made substantial contributions to conception and design of the study and acquisition of data. ZS made substantial contributions to conception and design, analysis and interpretation of data, drafting the manuscript and revising it critically for important intellectual content. CB made substantial contributions to conception and design, interpretation of data, and revised the manuscript critically for important intellectual content. All authors read and approved the final manuscript.
Ethics approval and consent to participate
This study was performed in accordance with the Declaration of Helsinki and was approved by the Peking Union Medical College Hospital Ethics Committee (no. ZS-1358). All patients were informed about the use of their tissue samples for biological research and provided written informed consent prior to the study.
Patient consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
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