**1. Introduction**

Cocoa beans, the seeds of the tree *Theobroma cacao* L., are the key raw material for chocolate production [1]. Criollo is the finest variety of cocoa [2]. Its seeds are aromatic, mild tasting, and with low bitterness: they represent the ideal raw material for a quality chocolate [1,3]. For obtaining chocolate from cocoa beans, one very important stage in the process is the roasting [4], which results in the production of desirable flavor and aroma compounds, as well as color changes [5]. The temperature and duration of roasting substantially affected the character in chemical and physical changes of cocoa beans [6].

Cocoa and chocolate have recently gained much attention due to their potential benefits in human health [1,7–10] and have recently become the target of increased scientific research due to their health promoting properties [11]. Cocoa, from a therapeutic viewpoint, is important due to the high concentrations of polyphenols as antioxidants [12]. These health effects have been assumed to be associated with the presence of polyphenols, among which are monomeric flavan-3-ols: epicatechin and catechin [5,13–16]. The beneficial effects of cocoa polyphenols to human health are, among others, the scavenge of free radicals and prevention of damage to DNA, the chelation of metals, vasoprotective effects, improvements in endothelial function, anti-inflammatory effects, the amelioration of insulin resistance, and anticarcinogenic effects, among others [13,17–21]. Cocoa is a rich source of polyphenolic compounds and may account for 12–18% of the dry mass of the beans [5]. Typically, polyphenols are sensitive to heat during the process, especially under a high temperature environment, i.e., roasting and drying [22,23]. Additionally, roasting influences the alteration of bioactive compounds [24]. In particular, the roasting process leads to the loss or modification of flavanols, which leads to a 14% loss of the total phenolic content (TPC), as well as the epimerization of epicatechin to catechin [25–32].

Kinetic modeling can provide a deeper understanding of the changes that occur during thermal processing controlling and food quality optimization [33,34]. Since these roasting techniques were introduced to the chocolate industry, the roasting time and temperature have been studied, applying designs or models that allow for a proper assessment of the process [35]. Various studies have been reported on the effect of roasting on cocoa nibs' coloration, physical/chemical changes [4,5,8,16,23,25,36], flavor changes [37], and the kinetics of polyphenol degradation during drying [12,28,38]; however, studies on the kinetics of the polyphenol and monomeric flavan-3-ol degradation of Criollo cocoa during the roasting process are scarce, so the aim of the present study was to understand this degradation and determine the kinetic parameters.

#### **2. Materials and Methods**

#### *2.1. Materials*

Approximately 14 kg of dried fermented cocoa beans of Criollo variety were obtained directly from Multi-Service Cooperative APROCAM in the Bagua province, Amazon region, Perú.

#### *2.2. Chemicals and Standards*

Methanol HPLC grade (JT Baker, Deventer, The Netherlands), Folin–Ciocalteu's phenol reagent, gallic acid, sodium carbonate, ≥98% (-)-epicatechin (HPLC) from green tea, ≥97% (-)-catechin (HPLC) from green tea, and ≥90% petroleum ether were purchased from Sigma Aldrich (Diessenhofen, Germany).

#### *2.3. Roasting Process*

Samples of cocoa bean were subjected to different treatments of time and temperature as conditions of the roasting process. Prior to the roast, the beans were selected according to their size, choosing beans of uniform size. Criollo cocoa samples (100 g) were roasted in a roaster (IMSA, ERTC-51, Lima, Perú) in the Agroindustry Plant Pilot at Universidad Nacional Toribio Rodríguez de Mendoza de Amazonas (UNTRM), Perú.

#### *2.4. Chemical Analysis*

#### 2.4.1. Methanolic Extraction of Phenolic Compounds and Monomeric Flavan-3-ols

According to Summa et al. [29] regarding some modifications, three cocoa beans were ground in a pestle from each sample. The powder was defatted by extraction with petroleum ether and centrifuged using a centrifuge (MPW Med Instruments, MPW-51, Warszawa, Poland) at 3000 rpm for 15 min at room temperature. The supernatant was then discarded. Fresh petroleum ether was added and then centrifuged (four times). The resulting defatted material was air dried at room temperature. Methanol extraction based on the methodology used by Jonfia-Essien et al. [30] with some modifications was performed, and 0.5 g of defatted cocoa powder was homogenized in 25 mL of 80% methanol for 30 min in a magnetic stirrer and then filtered in a vacuum filter. This methanolic extract was used for the determination of both TPC and monomeric flavan-3-ols.

#### 2.4.2. Total Phenolic Content

The TPC of the Criollo cocoa beans was determined following the procedures of Singleton et al. [31] and Hu et al. [7]. Diluted extract (0.1 mL) or blank (0.1 mL deionized water) was mixed with 7.9 mL water and 0.5 mL of Folin-Ciocalteu reagent for 5 min at 22 ◦C. Next, 1.5 mL of saturated sodium carbonate solution was added. Reagents were mixed thoroughly by vigorous shaking for 10 s by hand. The mixture was incubated in the dark at 22 ◦C for 2 h before determination of the absorbance at 765 nm using an UV/Visible spectrophotometer (Unico, S2100, Dayton, NJ, USA). Gallic acid in 70% methanol was diluted (2–16 mg/L) to create a calibration curve. TPC is expressed as mg of gallic acid equivalents/g defatted cocoa bean (mg GAE/gdf).

#### 2.4.3. Quantification of Epicatechin and Catechin of the Methanolic Extract

Quantification of epicatechin and catechin followed the procedures of Wang et al. [39] with some modifications in a high performance liquid chromatography system (HPLC) (Lachrom Elite, Hitachi, Japan) equipped with a UV-Vis detector (Hitachi, Lachrom Elite L–2420, Wako, Japan) and isocratic pump (Hitachi, Lachrom Elite L–2130, Japan). The column used was a C18 150 × 4.6 mm, 5 μm (Merck, Purospher RP–18 endcapped, Darmstadt, Germany). The mobile phase was methanol/water/orthophosphoric acid (20/79.9/0.1), and the flow rate was 1 mL/min. Absorption wavelength was selected at 210 nm. The sample injection volume was 20 μL. Chromatographic peaks in the samples were identified by comparing their retention time and UV spectrum with those of the reference standards. A standard graph for each component was prepared by plotting concentration versus area. Quantification was carried out from integrated peak areas of the sample and corresponding standard graphs, and the epic/cat ratios of epicatechin and catechin concentrations were obtained.

#### 2.4.4. TPC and Monomeric Flavan-3-ol Degradation

According to Martins et al. [40], the degradation was then calculated according to the following formula:

$$\% \text{ degradation} = \left(\frac{X\_{\text{control}} - X\_t}{X\_{\text{control}}}\right) \ast 100 \tag{1}$$

where *Xcontrol* is the TPC, epicatechin, or catechin concentration in the control sample (unroasted cocoa bean), and *Xt* is the TPC, epicatechin, or catechin concentration at time *t*.

## *2.5. Experimental Desing for Kinetics of TPC and Monomeric Flavan-3-ols*

Samples (100 g) in triplicate of Criollo cocoa beans were roasted for each combination of time and temperature (10, 20, 30, 40, and 50 min at 90, 110, 130, 150, 170, 190, and 200 ◦C), 105 samples were obtained. Means and standard deviations were calculated. The temperature and time values used were defined according to the published literature. Fitting procedures (Matlab 2014) were used to determine the reaction rate constants for TPC, epicatechin, and catechin. The general kinetic models of zero-, first-, and second-order reactions were used, presented in Equations (2)–(4), respectively [41].

$$[A]\_0 - [A] = kt \tag{2}$$

$$[A] = [A]\_0 \exp(-kt) \tag{3}$$

$$\frac{1}{\left[A\right]} - \frac{1}{\left[A\right]\_0} = \text{ } kt\tag{4}$$

*k* (min−1) is the reaction rate constant at temperature T; *t* is the reaction time (min); [*A*]0 and [*A*] are the initial (control sample) and final amounts of TPC, epicatechin or catechin, respectively, at different times *t* and temperatures.

The final fitting models were obtained by matching and analyzing the initial models obtained by Matlab software with general equations of zero-, first-, and second-order reactions presented in Equations (2)–(4), respectively. The effect of temperature was evaluated by means of the Arrhenius equation.

$$k = k\_0 \* \mathfrak{e}^{(-E\_a/RT)}.\tag{5}$$

The activation energy *Ea* for the formation of each parameter was determined by linear regression of *Ln k* curve versus 1/*T* with Equation (6).

$$Lnk = Lnk\_0 - E\_a/RT\tag{6}$$

*Ea* (J/mol) is the apparent activation energy, *R* (8.3145 J/mol.K) is the universal gas constant, *k* is the reaction rate constant, and *k*0 is the pre-exponential factor.

#### *2.6. Statistical Analysis*

The results were compared using one-factor analysis of variance (ANOVA) followed by the Tukey test. Previously, Dixon's Q test, for the identification and rejection of outliers, was used. Statistical analyses were carried out in Minitab 17 software.

#### **3. Results**

### *3.1. E*ff*ect of Roasting on Monomeric Flavan-3-ols and TPC*

The TPC, epicatechin, and catechin concentration and the epi/cat ratio of the unroasted (control sample) Criollo cocoa beans were 110.98 ± 1.43 mg GAE/gdf, 30.29 ± 1.0 mg/gdf, 2.71 ± 0.13 mg/gdf, and 11.20 ± 0.40, respectively (Table 1). These values served as a starting point for the study of degradation. The TPC and epicatechin values and the epi/cat ratios of the treatments were lower than the control sample. Some results show that the concentration of epicatechin in some cases increased. During the roasting process, the epi/cat ratio was reduced as the process temperature increased. Considering a time of 10 min, the epi/cat ratio was reduced from 6.08 ± 0.96 when heated at 90 ◦C to 3.20 ± 0.68 at 200 ◦C. Table 1 shows that epicatechin degradation reached 100% when Criollo cocoa beans were roasted for 50 min at 190 or 200 ◦C, while TPC did not reach full degradation at any temperature or time. The opposite happened with catechin, which showed patterns of formation. The values in bold indicate that there was formation instead of degradation.

43

1 Different letters in the same column represent statistically significant differences (*p* ≤ 0.05). At least three replicate samples were analyzed. 2 Bold values indicate an increase in

catechin

concentration.

*Antioxidants* **2020**, *9*, 146

#### *3.2. Roasting Kinetics of Monomeric Flavan-3-ols and TPC*

The values obtained from the fitted parameters are given in Table 2. In the case of TPC and epicatechin, the *k* value increases from 0.02 ± 0.01 min−1 at 90 ◦C to 0.10 ± 0.05 min−1 at 200 ◦C, and from 0.02 ± 0.01 min−1 at 90 ◦C to 0.13 ± 0.04 min−1 at 200 ◦C, respectively. The kinetic parameters of the catechin are not shown because it corresponds to a combined production and degradation model, which does not correspond to Equations (2), (3), or (4).

**Table 2.** First-order kinetic parameters fitted for TPC and epicatechin degradation.

Figure 1 shows that the catechin reaction kinetics could be divided into two order models: one for formation and the other for degradation, where the highest catechin formation (**84.90** ± **37.90%**) was at 170 ◦C and 30 min of roasting.

**Figure 1.** Kinetic of formation and degradation of catechin.

The temperature dependence of the TPC and epicatechin degradation was estimated using the Arrhenius equation expressed in Equation (5). The linear behavior of *Ln k* versus 1/T allowed us to determine *Ea* for TPC and epicatechin of 24.03 J/mol (R2 = 0.94) and 22.51 J/mol (R2 = 0.97), respectively (Figure 2). The parameters for catechin kinetics were not calculated because it presented a degradation and production pattern, as mentioned above.

**Figure 2.** Correlation of *Ln k* with 1/T to obtain the TPC and epicatechin kinetic parameters.

#### **4. Discussion**

The time and temperature of the roasting process depend on several factors, such as the type of cocoa (Criollo or Forastero) and others [42]. The main type of polyphenols (known for their demonstrated antioxidant and anti-inflammatory properties) in cocoa is flavanols. This family of compounds includes catechin and epicatechin (monomeric species). Epicatechin is the most abundant flavanol in cocoa and accounts for 35% of the total polyphenolic fraction [43,44] (TPC). In one study made by Kim and Keeney [45], the epicatechin concentrations ranged from 2.66 (Jamaica) to 16.52 mg/g (Costa Rica) of the defatted sample in cocoa beans of different varieties. The epicatechin and catechin concentrations of the control sample were higher than those found by Kim and Keeney [45] and Payne et al. [32] in fermented cocoa beans (the Forastero variety) from Ivory Coast (epicatechin: 1.69 ± 0.10 mg/g; catechin: 0.08 ± 0.00 mg/g) and Papua New Guinea (epicatechin: 0.78 ± 0.04 mg/g; catechin: 0.05 ± 0.00 mg/g). These values were higher than those found by Mazor Joli´c et al. [46] in Forastero cocoa beans from Ghana (epicatechin: 2.23 ± 0.6 mg/g; catechin: 0.28 ± 0.04 mg/g). Other studies conducted in Perú on fermented cocoa beans from Tingo María, San Alejandro, and Curimaná presented epicatechin and catechin concentrations of 0.33–5.04 mg/g and 0.02–0.14 mg/g, respectively [47]. It is known that epicatechin has diverse biological properties (antioxidant, antimicrobial, anti-inflammatory, antitumor, and cardio-protective activity) [48]; thus, the Criollo cocoa beans used in the present study show potential for use in the elaboration of functional foods. It has been postulated that the ratio of epicatechin to catechin (epi/cat) possibly could be associated with the degree of cocoa processing [32,49]. Payne et al. [32] obtained epi/cat ratio values of 20.1 ± 0.63 for dry fermented cocoa beans and 3.35 ± 0.20 at 90 ◦C to 0.96 ± 0.02 at 120 ◦C for roasted beans. Table 1 shows control sample epi/cat ratio values of 11.20 ± 0.40; considering 50 min of roasting, values of 5.55 ± 1.24 at 90 ◦C to 2.36 ± 0.20 at 130 ◦C and 0.00 ± 0.00 at 200 ◦C were obtained. These results demonstrate that roasting at 200 ◦C at any time (10 or 50 min) is aggressive for epicatechin, so if these parameters are used, the functional properties of chocolate will be lost.

In Table 1, a roasting time of 50 min at 90, 110, 130, 150, 170, 190, and 200 ◦C degraded the TPC to 53.46 ± 7.29, 46.88 ± 6.30, 65.40 ± 3.16, 74.57 ± 1.37, 82.40 ± 0.89, 88.91 ± 1.27, and 92.29 ± 0.06, respectively; these results are consistent with those obtained by Mazor Joli´c et al. [46]. The decrease of TPC is associated with the thermal and oxidative degradation of these compounds [35]. It was proven that, at high temperature, low molecular weight phenolic compounds easily volatilize [50]. Djikeng et al. [50] also observed that the degradation percentage of the TPC also increased with roasting time. In general, more intense roasting conditions result in a greater loss of TPC due to the high redox activity of polyphenols at those conditions [50,51]. Epicatechin degraded more than TPC at a higher temperature, obtaining a complete degradation at 200 ◦C and 50 min. This is concordant with Stanley et al. [5], who observed the significant effects of roasting time within temperatures up to 190 ◦C; the levels of epicatechin in Trinitario cocoa (a type of fine cocoa) decreased in a time- and temperature-dependent manner. Significant decreases in Criollo cocoa were observed. Among the monomeric flavan-3-ols, epicatechin was identified as the more active compound responsible for the vascular health benefits associated with cocoa and chocolate [52]. Applying treatments of 190–200 ◦C results in a total loss of epicatechin, and the healthy properties of the Criollo cacao and its aromatic properties may also be reduced; for this reason, Zy˙ ˙ zelewicz et al. [1] and Hurst et al. [36] state that Criollo beans require milder roasting conditions. The roasting temperature is generally in the range from 110 to 120 ◦C.

The most important reactions occurring with catechins under thermal processing are epimerization, hydrolysis, oxidation, and polymerization; catechin epimerization takes place on two asymmetric carbon atoms in the C ring [53]. In Table 1, catechin shows a particular behavior due to the increase in its concentration (formation) at the beginning of the roasting. The catechin values in bold font shows an increase in its concentration with respect to the control sample (unroasted beans) during the first 10 min at 130 ◦C (**11.46** ± **4.71%**), and its formation continues until the first 10 min at 200 ◦C and subsequently degrades to 21.37 ± 5.74%. The trend of these results is consistent with those obtained by Stanley et al. [5], except that the catechin formation was much higher, showing an increase in catechin concentration 675% higher than the control (unroasted beans) at 10 min. These results were reported for Trinitario cocoa beans. We can affirm that, in Trinitario cacao, the formation of catechin is much greater than in Criollo cacao, and this applies to the epimerization of epicatechin to catechin [5,14,25,32,54]; however, as the treatment continued, its concentration decreased. These results were also found by Zy ˙ ˙ zelewicz et al. [4] in Forastero cocoa, since after 15 min of roasting at 135 ◦C, the content of catechin increased significantly, initiating its degradation. When cocoa beans are progressively roasted at conditions described as low, medium, and high roast conditions (160 ◦C at 13, 20, and 25 min), there is a progressive loss of epicatechin and an increase in catechin with higher roast levels [36,55]. The high temperature–short time (HTST) process induces higher epicatechin epimerization than does the low temperature–long-time (LTLT) process, generating greater amounts of catechin [16,53].

Cocoa consumption is suggested to promote health benefits. The amounts and profiles of monomeric flavanols depend strongly on the bean type, origin, and manufacturing process. Roasting is known as a crucial step in the technical treatment of cocoa, which leads to flavanol losses and modifications, especially the epimerization of epicatechin to catechin [42]. These modifications were fully produced (epimerization) at temperatures of 150, 170, and 190 ◦C (Table 1). In the manufacture of chocolate, it is necessary to achieve a balance that maintains healthy properties and the development of the aroma that characterizes fine, Criollo cocoa chocolates.

In order to explain the phenomena of TPC and epicatechin degradation, the data were fitted using kinetic models, which are pseudo first-order kinetic reactions coinciding with the majority of food reactions [56]. The term "pseudo" is added with respect to reactions in biological materials, such as food, because the actual reaction mechanism and its kinetics are far more complex [28]. We used seven temperatures in our experiment, allowing us to notice the behavior of the roasting process; when Taoukis et al. [56] states that five or six experimental temperatures are the practical optimum to obtain meaningfully narrow confidence limits in kinetic parameters (*Ea* and *k*). The values obtained from the fitted parameters in Matlab are given in Table 2 and consistent with previous studies [26,28,41,57], in which the first-order kinetic model explains both TPC and epicatechin degradation at all temperatures; the *k* value of epicatechin is higher than the *k* value of TPC. These results suggest that the degradation of epicatechin is faster than the degradation of TPC. Roasting conditions of 130 ◦C and 10 min produced a lower percentage of epicatechin (8.05 ± 3.01%) and TPC (10.98 ± 6.04%) degradation. Many authors claim that these compounds are degraded by heat [4,22,23,50]. The most intense temperatures have caused significant degradation of the components; however, it was found that the epicatechin is more thermosensitive than TPC. This is explained by the greater *k* value that epicatechin has compared to TPC; however, the chemical basis of this phenomenon must be studied.

Kinetic modeling may also use the influence of processing on critical quality parameters. Knowledge of degradation kinetics, including the reaction order, *Ea* and *k*, is of great importance to predict food quality loss during thermal process treatments [58]. Food quality loss reactions described by the kinetic models were shown to follow Arrhenius (Equation (5)) behavior with temperature

changes [56]. The *Ea* for TPC was greater than the *Ea* for epicatechin, this being the minimal value of energy that a specific collision between reagent molecules must achieve in order for a reaction to take place [59]. The epicatechin thus needs less energy than TPC to degrade it. The relative differences in *Ea* values could be due to the different composition of the sample studied or to changes occurring in the samples during heating [58]. Olivares-Tenorio et al. [60] observed that catechin followed patterns of formation by epimerization and degradation, and experiments need to be devised to unravel these various reactions. Figure 1 shows that catechin reaction kinetics could be divided into a two order models: one for formation and the other for degradation, where the highest catechin formation was at 170 ◦C and 30 min (**84.90** ± **37.90%**), followed by treatments at 190 ◦C and 150 ◦C. The largest catechin formation occurs in the first 30 min of roasting at any of the aforementioned temperatures. The epimerization from epicatechin to catechin due to technological treatment (heat) has often been postulated. Only a few publications have confirmed this reaction by enantioseparation. The reaction mechanism is not fully clarified, but it is assumed that ring opening occurs at position C-2 of the oxygenated ring, and reclosing leads to the atypical enantiomers. High temperatures, particularly when combined with alkaline conditions, accelerate the epimerization reaction [42]. Figure 1 shows that at 90 and 110 ◦C there is only catechin degradation; subsequently, the formation of catechin begins slowly from 130 ◦C, reaching its highest point at 190 ◦C. The treatment at 200 ◦C only shows a degradation pattern. It is assumed that the *k* for catechin formation kinetics will be greater at 190 ◦C for 30 min, after which the degradation kinetics will take place at a high *k*. An enantoseparation reaction will occur to a greater extent for 30 min when the Criollo cocoa beans are roasted at 150, 170, or 190 ◦C.

#### **5. Conclusions**

The roasting process of Criollo cocoa allowed for a higher formation of catechin in the first 30 min of the process at 170 ◦C, followed by degradation to minimum levels at 200 ◦C, while the epicatechin showed greater susceptibility to treatment than the TPC and the catechin. Likewise, the degradation data of TPC and epicatechin are better suited to a first-order kinetic reaction as the temperature increases. Although it is true that the roasting stage is essential for the development of the aromas of chocolate, this also affects the polyphenolic content and monomeric flavan-3-ol of cocoa beans and thus the final chocolate product; therefore, roasting at moderate temperatures is necessary to obtain minimal degradation of cocoa phenolic compounds and consequently antioxidant properties. Likewise, with epicatechin being part of the total phenolic compounds, its degradation percentage is higher. This is explained by its kinetics; however, its chemical explanation for future work is pending.

**Author Contributions:** Conceptualization, E.M.C.-A. and F.P.C.-T.; methodology, E.F.-R. and S.G.C.-Q.; software, E.M.C.-A.; formal analysis, E.M.C.-A. and F.P.C.-T.; investigation, E.F.-R.; writing—original draft preparation, E.F.-R.; writing—review and editing, E.M.C.-A., R.S., and F.P.C.-T.; supervision, F.P.C.-T. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by Project N◦16808-2016/PROYECTO-CACAO, of the Programa Nacional de Innovación Agraria (PNIA) of the Instituto Nacional de Innovación Agraria of the Peruvian Government, the Universidad Nacional Toribio Rodríguez de Mendoza de Amazonas, and the Pontificia Universidad Católica del Perú.

**Acknowledgments:** A special acknowledgement to Multi-Service Cooperative APROCAM for the permission for the use of their facilities.

**Conflicts of Interest:** The authors declare that there is no conflict of interest.

#### **References**

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

*Article*


**Cooperative Development and Land Use Planning Dynamics in Sub-Saharan Africa: A Quest for Socio Solidarity Economy and Partnership in Sustainable Urban Development**

W. Magigi, K. Faustine Bee and E. B. Danda *Moshi University College of Cooperative and Business Studies A Constituent College of Sokoine University of Agriculture, Moshi-Kilimanjaro Tanzania*


*Article* **Identification of Blood-Based Biomarkers for the Prediction of the Response to Neoadjuvant Chemoradiation in Rectal Cancer**

**Delphine Dayde 1, Jillian Gunther 2, Yutaka Hirayama 3, David C. Weksberg 2,4,†, Adam Boutin 5, Gargy Parhy 1, Clemente Aguilar-Bonavides 6, Hong Wang 7,8,†, Hiroyuki Katayama 7, Yuichi Abe 9, Kim-Anh Do 6, Kazuo Hara 10, Takashi Kinoshita 11, Koji Komori 11, Yasuhiro Shimizu 11, Masahiro Tajika 3, Yasumasa Niwa 3, Y. Alan Wang 5, Ronald DePinho 5, Samir Hanash 7, Sunil Krishnan 2,12,† and Ayumu Taguchi 1,9,13,\***

**Simple Summary:** Although pathologic complete response (pCR) to neoadjuvant chemoradiation (nCRT) in locally advanced rectal cancer (LARC) is associated with better outcomes, a subset of tumors exhibit resistance to nCRT. Therefore, there is a need of biomarkers to predict the nCRT response and increment efforts for personalized therapeutic options. To this end, we analyzed pretreatment plasma proteome of a mouse model of rectal cancer treated with concurrent chemoradiation, resulting in identification and validation of plasma VEGFR3 as a potential predicting biomarker. In addition, plasma levels of EGFR and COX2, previously validated tissue-based predicting biomarkers, were significantly higher in non-pCR than pCR LARC patients, indicating that EGFR and COX2 can also serve as blood-based biomarkers. The performance of the biomarker panel combining VEGFR3, EGFR, and COX2 were significantly improved compared to that of each marker alone, providing a rationale for further integration and refinement of the biomarker panel and validation in the larger sample sets.

**Abstract:** The current standard of care for patients with locally advanced rectal cancer (LARC) is neoadjuvant chemoradiation (nCRT) followed by total mesorectal excision surgery. However, the response to nCRT varies among patients and only about 20% of LARC patients achieve a pathologic complete response (pCR) at the time of surgery. Therefore, there is an unmet need for biomarkers that could predict the response to nCRT at an early time point, allowing for the selection of LARC patients

**Citation:** Dayde, D.; Gunther, J.; Hirayama, Y.; Weksberg, D.C.; Boutin, A.; Parhy, G.; Aguilar-Bonavides, C.; Wang, H.; Katayama, H.; Abe, Y.; et al. Identification of Blood-Based Biomarkers for the Prediction of the Response to Neoadjuvant Chemoradiation in Rectal Cancer. *Cancers* **2021**, *13*, 3642. https:// doi.org/10.3390/cancers13143642

Academic Editor: Takaya Shimura

Received: 4 June 2021 Accepted: 16 July 2021 Published: 20 July 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

who would or would not benefit from nCRT. To identify blood-based biomarkers for prediction of nCRT response, we performed in-depth quantitative proteomic analysis of pretreatment plasma from mice bearing rectal tumors treated with concurrent chemoradiation, resulting in the quantification of 567 proteins. Among the plasma proteins that increased in mice with residual rectal tumor after chemoradiation compared to mice that achieved regression, we selected three proteins (Vascular endothelial growth factor receptor 3 [VEGFR3], Insulin like growth factor binding protein 4 [IGFBP4], and Cathepsin B [CTSB]) for validation in human plasma samples. In addition, we explored whether four tissue protein biomarkers previously shown to predict response to nCRT (Epidermal growth factor receptor [EGFR], Ki-67, E-cadherin, and Prostaglandin G/H synthase 2 [COX2]) also act as potential blood biomarkers. Using immunoassays for these seven biomarker candidates as well as Carcinoembryonic antigen [CEA] levels on plasma collected before nCRT from 34 patients with LARC (6 pCR and 28 non-pCR), we observed that levels of VEGFR3 (*p* = 0.0451, AUC = 0.720), EGFR (*p* = 0.0128, AUC = 0.679), and COX2 (*p* = 0.0397, AUC = 0.679) were significantly increased in the plasma of non-pCR LARC patients compared to those of pCR LARC patients. The performance of the logistic regression model combining VEGFR3, EGFR, and COX2 was significantly improved compared with the performance of each biomarker, yielding an AUC of 0.869 (sensitivity 43% at 95% specificity). Levels of VEGFR3 and EGFR were significantly decreased 5 to 7 months after tumor resection in plasma from 18 surgically resected rectal cancer patients, suggesting that VEGFR3 and EGFR may emanate from tumors. These findings suggest that circulating VEGFR3 can contribute to the prediction of the nCRT response in LARC patients together with circulating EGFR and COX2.

**Keywords:** rectal cancer; neoadjuvant chemoradiation; mouse model; proteomics; biomarkers

#### **1. Introduction**

The current standard of care for patients with clinical stage II or III locally advanced rectal cancer (LARC), defined as T3–T4 or node-positive non-metastatic disease, is neoadjuvant chemoradiation (nCRT) followed by total mesorectal excision (TME) surgery to improve resectability, anal sphincter preservation, and long-term outcome [1,2]. However, the response to nCRT in LARC varies among patients. After nCRT, about 20% of LARC patients achieve a pathological complete response (pCR), which is associated with favorable 5-year disease-free survival compared to those without complete response (non-pCR) [3,4]. Conversely, while ~40% of LARC patients achieve a wide range of partial responses, a subset (~20%) of tumors exhibit resistance to nCRT, demonstrating either progression or only minimal regression/stable disease [5].

Given the achievement of pCR in a significant proportion of patients undergoing nCRT and the adverse effects of major TME surgery such as perioperative mortality, anastomotic leak, stoma-related complications, and long-term urinary and sexual dysfunction [6–8], there is a growing interest in organ preservation for LARC patients who achieve a clinical complete response (cCR) after nCRT. Since 2004, a series of studies have reported the promising potential of a watch-and-wait strategy to avoid major TME surgery [9–12]. Recent large datasets from meta-analyses and registry studies indicated that 5-year overall survival did not differ between patients treated with a watch-and-wait and those with surgery [13,14], suggesting that the watch-and-wait strategy can be an alternative to TME surgery with low oncological risk. However, the significant limitation of the watch-andwait strategy is the poor concordance between pCR and cCR, which can result in local regrowth after nCRT [13–15]. Therefore, there is an immense need for biomarkers for safe adoption of the watch-and-wait strategy to predict pCR and identify patients who may potentially avoid surgery after completion of nCRT.

A wide variety of clinical, pathologic, and radiologic factors, including tumor size, differentiation, clinical T and N stages, and tumor regression rates, have been associated with nCRT response [16–19]. Several potential tissue- or blood-based molecular biomarkers to predict the nCRT response, including DNA methylation, protein, miRNA, and cfDNA,

have also been described [20–23]. As carcinoembryonic antigen (CEA) is routinely used for disease monitoring in colorectal cancer, the relationship of CEA and the nCRT response has been most widely studied. A recent meta-analysis suggest that pretreatment CEA levels were significantly and inversely correlated with the rate of pCR [24]. However, to date, none of these clinicopathological, radiological, or molecular biomarkers have yet reached the clinic due to inadequate sensitivity and specificity.

In this study, we sought to identify blood-based biomarkers that can differentiate patients who will achieve a pCR versus non-pCR by analyzing the plasma proteome of a mouse model with rectal cancer that recapitulate molecular and biological features of human rectal cancer [25]. This approach allows minimization of extraneous variability and blood sampling at defined pre-therapeutic time points during the course of tumor progression [26]. In addition, we investigated whether previously validated tissue-based biomarkers for nCRT response [20] can also serve as blood-based biomarkers for predicting pCR.

#### **2. Materials and Methods**

#### *2.1. Mouse Model*

All animal experiments were conducted in accordance with institutional and national guidelines and regulations with approval by the Institutional Animal Care and Use Committee at The University of Texas MD Anderson Cancer Center. At 8 weeks of age, mice were administered with a 4-hydroxytamoxifen (4-OHT) enema (1 mg/mL) and doxycycline [25]. Beginning at 4 weeks post-induction, tumor size was measured as the percentage of tumor occlusion of the lumen by weekly colonoscopy. Once the percentage of tumor occlusion of the lumen reached 50%, intraperitoneal injection of 5-FU (30 mg/kg) and concurrent radiation (5 Gy per fraction) were administered to four mice with rectal tumors for 5 consecutive days, resulting in two mice showing complete tumor regression and two mice with residual tumors. Four control mice at the same age without Cre DNA recombinase were also treated with the same regimen. Five days prior to the treatment, plasma was collected, and Magnetic resonance imaging (MRI) was performed to assess rectal tumors. Mice were euthanized a week after chemoradiation and response to chemoradiation was pathologically evaluated.

#### *2.2. Mass Spectrometry Analysis of Pretreatment Mouse Plasma Samples*

An independent pool of pretreatment plasmas from two mice with rectal tumors achieving regression, two mice with residual rectal tumors after chemoradiation, and four control mice were created. Each pool of mouse plasma was subjected to immunodepletion, whereby the top three abundant proteins (albumin, IgG, and transferrin) were removed using an Immunodepletion column (Agilent Technologies, Santa Clara, CA, USA). The remaining low abundant proteins in each sample were treated with 25 mM tris(2-carboxyethyl)phosphine (TCEP) for Cys reduction and subsequently labeled with Iodoacetyl Tandem Mass Tag (IodoTMT) sixplex isobaric label reagent (Thermo Fisher Scientific, Waltham, USA). The mixture of labeled samples was separated by an orthogonal two-dimensional high-performance liquid chromatography (2D-HPLC) system (Shimadzu, Kyoto, Japan) with eight fractions of anion-exchange (Agilent Technologies, Santa Clara, USA) as the first dimension followed by 12 reversed-phase fractions by RPGS reversedphase column (4.6 mm I.D. × 150 mm, 15 μm, 1000 Å, Column Technology Inc, Fremont, CA, USA) as the second dimension. Collected protein fractions were lyophilized, digested with trypsin, and analyzed by nano LC–high definition MSE (HDMSE) with Synapt G2Si ion-mobility quadrupole time-of-flight (Q-TOF) mass spectrometry (Waters, Milford, USA).

The 2 h gradient elution was performed in a capillary column (C18, 3 μm 120 Å, 75 μmID × 25 cmL, Column Technology, Inc., Fremont, USA) at 500 nl/min with the mobile phase A 0.1% formic acid (FA) in 2% acetonitrile (ACN) and B 0.1% FA in 98% ACN. The mass spectrometer was operated with a resolving power of at least 20,000 full width at half maximum (FWHM) at m/z 785.843 (+2, Glu1-fibrinopeptide B) nano electrospray

ionization (ESI) source with a NanoLockSpray. The lock mass channel was sampled every 60 s.

Accurate LC-HDMSE data were acquired in an alternating low energy (HDMS) and high energy (HDMSE) mode with mass scan range from m/z 50 to 1800 under a capillary voltage of 2.8 kV, a source temperature of 100 ◦C, and a cone voltage of 30 V. The spectral acquisition in each mode is 1.0 s with a 0.1 s inter-scan. In HDMS mode, data are collected at a collision energy of 2 eV in both Trap and Transfer cell. In HDMSE mode, the collision energy is ramped up from 25 to 55 eV in the Transfer cell. The acquired data were processed through ProteinLynx Global Server (PLGS, WATERS, Milford, USA) and searched against the Uniprot mouse database at 4% false discovery rate (FDR). The identified proteins were filtered with ≤ 5 ppm mass accuracy of sequenced peptides. Quantile normalization approach was used to normalize the peak intensities of reporter ions before protein quantification.

#### *2.3. Human Plasma Samples*

All human plasma samples were obtained following Institutional Review Board approval and informed consent. Plasma samples were collected from 34 treatment-naïve LARC patients undergoing neoadjuvant chemoradiation (50.4 Gy in 28 daily fractions of 1.8 Gy with concurrent capecitabine (an oral prodrug of 5-fluorouracil)) at the MD Anderson Cancer Center, and used for validation of biomarker candidates (pretreatment LARC set). After completion of chemoradiation, patients underwent surgical excision, and standardized pathological procedures were followed for the assessment of residual disease. An independent set of plasma samples collected at the time of diagnosis and 5 to 7 months after surgery from 18 surgically resected rectal cancer patients at the Aichi Cancer Center was used to assess the association between biomarker candidates and surgical resection (pre-and post-surgery RC set). All patients in the pre- and post-surgery RC set were treated by surgery alone.

#### *2.4. Luminex Assays*

Levels of Vascular endothelial growth factor receptor 3 (VEGFR3), Insulin like growth factor binding protein 4 (IGFBP4), Carcinoembryonic antigen (CEA), Epidermal growth factor receptor (EGFR), and E-cadherin were measured using the Luminex kit (HANG2MAG-12K, HIGFBMAG-53K, HCCBP1MAG-58K, and HSCRMAG-32K from Millipore, and EPX010-12315-901 from Life Technologies), according to the manufacturer's instructions. Each sample was assayed in duplicate, and the absorbance was measured with a calibrated Bio-Plex machine (Bio-Plex MAGPIX System, Bio-Rad, Hercules, CA, USA).

#### *2.5. ELISA Assays*

Levels of Cathepsin B (CTSB), Ki-67, and Prostaglandin G/H synthase 2 (COX2) were determined using enzyme-linked immunosorbent assay (ELISA) kits (ab119584 from Abcam, Cambridge, UK; DY7617-05 from R&D Systems, Minneapolis, MN, USA; and RAB1034-1KT from Sigma-Aldrich, St. Louis, MO, USA) and according to the manufacturer's protocol. For all ELISA experiments, each sample was assayed in duplicate, and the absorbance was measured with a FLUOstar Omega microplate reader (BMG Labtech, Ortenberg, Germany).

#### *2.6. Statistical Analysis*

Categorical data were compared by Fisher's exact test or a chi-square test using Prism 7.05/e software (GraphPad). For all Luminex and ELISA assays, an internal control sample was run in every plate, and each value of the samples was divided by the mean value of the internal control in the same plate to correct the interplate variability. Individual biomarker performance was assessed using the Welch's *t*-test or the paired *t*-test. Sensitivity, specificity, and the area under the curve (AUC) were determined by a receiver operating characteristic

analysis. The likelihood ratio test was employed to assess the significance of the model based on the biomarker panel combining VEGFR3, EGFR, and COX2.

#### **3. Results**

#### *3.1. Proteomic Profiling of Pretreatment Plasmas from a Mouse Model of Rectal Cancer Treated with Chemoradiation*

To faithfully recapitulate the development of human locally advanced rectal cancer (LARC), we utilized a genetically engineered mouse model of colorectal cancer, which harbors a Doxycycline (Dox)-inducible oncogenic Kras allele and conditional null alleles of Apc and Trp53 (iKAP) [25]. Approximately 25% of iKAP mice are expected to develop only rectal tumors by colon-specific activation of Cre DNA recombinase with rectal enema of 4-OHT. Occurrence of rectal tumors in Dox-treated iKAP mice was confirmed by endoscopy, MRI, and biopsy (Figure 1A). iKAP mice with rectal tumors and control mice without Cre DNA recombinase received intraperitoneal injection of 5-FU (30 mg/kg) and concurrent radiation (5 Gy per fraction) for 5 consecutive days (Figure 1B). To assess the response to chemoradiation, mice were sacrificed a week after the treatment. Figure 1C depicts macroscopic images of colons of iKAP mice with residual tumors (non-regression) (#1) and with complete tumor regression (Regression) (#2), while rectal tumors of #1 and #2 mice were not distinctively different before chemoradiation (Figure 1A).

To identify plasma proteins that can predict pCR before chemoradiation, plasma samples were collected 5 days prior to chemoradiation from iKAP mice with rectal tumors and control mice (Figure 1B). For mass spectrometry analysis, plasma samples from two Regression mice, two Non-regression mice, and four control mice were respectively pooled. In-depth quantitative proteomic analysis with using tandem mass tags (TMT) labeling resulted in quantification of 567 proteins (393 unique genes). To gain insights into the underlying biological difference of increased plasma proteins in Regression mice and Nonregression mice, we performed a pathway analysis of proteins with more than a two-fold increase in the plasma of either Regression mice or Non-regression mice compared to control mice using WebGestalt (http://www.webgestalt.org/, accessed on 25 March 2020) [27]. Over-Representation Analysis based on KEGG (https://www.genome.jp/kegg/, accessed on 25 March 2020) [28] resulted in identification of 5 and 13 pathways that were significantly associated with increased proteins in the plasma of Regression mice or Non-regression mice compared to control mice, respectively (*p* values < 0.05, hypergeometric test, and with three or more overlapping proteins) (Figure 2A and Supplementary Table S1). While four pathways commonly identified in the comparison of Regression vs. control and Non-regression vs. control were associated with metabolism, nine pathways that were uniquely identified in the comparison of Non-regression vs. control were associated with the immune system and infectious disease (Complement and coagulation cascades; Antigen processing and presentation; Staphylococcus aureus infection; Pertussis), intracellular transport and catabolism (Lysosome; Phagosome), focal adhesion (Focal adhesion; Proteoglycans in cancer), and the endocrine system (Thyroid hormone synthesis) (Figure 2A,B).

**Figure 1.** Chemoradiation treatment of iKAP mice with rectal tumors. (**A**). Endoscopic images (top) and MRI scans (bottom) of rectal tumors. Red arrows in MRI scans indicate rectal tumors. (**B**). Outline of the experimental design for intraperitoneal injection of 5-FU and concurrent radiation. (**C**). Macroscopic images of colons of iKAP mice with residual tumor (Non-regression) (#1) and with complete tumor regression (Regression) (#2). A red arrow indicates residual tumors. CRT: chemoradiation.

**Figure 2.** Proteomic analysis of pretreatment plasmas from iKAP mice with complete tumor regression and with residual tumors. (**A**). Venn diagrams of pathways are significantly associated with more than a two-fold increase of proteins in the plasma of Regression mice or Non-regression mice compared to control mice. (**B**). Pathways uniquely identified in the comparison of Non-regression vs. control. *p* values were calculated by hypergeometric test. (**C**). Schema of mouse Vegfr3, Ctsb, and Igfbp4. Gray bars indicate peptides identified in mouse plasma. Numbers indicate mass spectra counts for each peptide. The amino acid sequences are based on P35917-1 for Vegfr3, P10605-1 for Ctsb, and P47879-1 for Igfbp4.

To determine biomarker candidates for prediction of pCR, we applied the following criteria: (1) the number of quantified peptides ≥ 10, (2) Non-regression/Control ratio > 3, and (3) Non-regression/Regression ratio > 3. Nine proteins passed these criteria (Table 1), and intriguingly, Collagen type I alpha 1 chain (Col1a1), Cathepsin B (Ctsb), and Vascular endothelial growth factor receptor 3 (Vegfr3) were included in the pathways uniquely associated with increased plasma proteins in Non-regression mice (Supplementary Table S1), suggesting a possible biological link between these proteins and rectal tumors resistant to chemoradiation. In addition, Insulin like growth factor binding protein 4 (Igfbp4) is of interest, as it can bind to and modulate the function of Insulin-like growth factor I (Igf1) [29], which is also included in two pathways associated with focal adhesion (Supplementary Table S1). Our mass spectrometry analysis yielded substantial peptide coverage for Vegfr3, Ctsb, and Igfbp4 (Figure 2C and Table 1), and therefore, we selected these three proteins for validation in human plasma samples.

#### *3.2. Validation of Protein Biomarker Candidates in a Set of LARC Plasma Samples*

VEGFR3, CTSB, and IGFBP4, as well as Carcinoembryonic antigen (CEA) were assayed in the pretreatment LARC set consisting of plasma samples collected prior to nCRT from 34 patients with LARC. In the pretreatment LARC set, 6 (17.6%) patients achieved pCR (pCR) and 28 (82.4%) patients had residual tumors after nCRT (non-pCR). While non-pCR LARC patients had more advanced T stages, clinical factors did not show a statistically significant difference between pCR and non-pCR LARC patients (Table 2).

**Table 1.** Plasma proteins increased in Non-regression iKAP mice.

Igfbp4: Insulin like growth factor binding protein 4, Col1a1: Collagen type I alpha 1 chain, Prdx6: Peroxiredoxin-6, Park7: Parkinson disease protein 7, F13b: Coagulation factor XIII B chain, Flna: Filamin-A, Ctsb: Cathepsin B, Vegfr3: Vascular endothelial growth factor receptor 3, Blvrb: Biliverdin reductase B.

*p* values were calculated by Fisher's exact test or a chi-square test.

In addition to three selected biomarker candidates, as many potential tissue-based biomarkers have been associated with nCRT response [20], we sought to determine whether tissue-based protein biomarkers in blood also can have the potential to predict pCR. According to prior studies, we selected four proteins, including Epidermal growth factor receptor (EGFR), Ki-67, Prostaglandin G/H synthase 2 (COX2), and E-cadherin, for testing in the pretreatment LARC set.

Plasma levels of VEGFR3, EGFR, and COX2 were significantly higher in non-pCR LARC compared to pCR LARC (VEGFR3: *p* = 0.0451, EGFR: *p* = 0.0128, COX2: *p* = 0.0397, Welch's *t*-test) (Figure 3A). CEA and other four biomarker candidates were not significantly different between pCR and non-pCR LARC patients (CEA: *p* = 0.1940, CTSB: *p* = 0.7894, IGFBP4: *p* = 0.3469, Ki-67: *p* = 0.1547, E-cadherin: *p* = 0.3836, Welch's *t*-test).

**Figure 3.** Validation of biomarker candidates in the pretreatment LARC set. (**A**). Levels of VEGFR3, CTSB, IGFBP4, CEA, EGFR, Ki-67, COX2, and E-cadherin in plasmas collected before nCRT from LARC patients who achieved pCR (pCR; *n* = 6) and LARC patients who had residual tumors after nCRT (non-pCR; *n* = 28). Horizontal lines indicate mean and standard deviation. *p* values were calculated using Welch's *t*-test. (**B**). Receiver operating characteristic curves for VEGFR3, EGFR, COX2, and their combination in the pretreatment LARC set. CEA: Carcinoembryonic antigen, EGFR: Epidermal growth factor receptor, COX2: Prostaglandin G/H synthase 2, AUC: the area under the curve.

We explored whether a combination rather than individual biomarkers allows for better discrimination of pCR and non-CR. The combination of VEGFR3, EGFR, and COX2 using logistic regression yielded an area under the curve (AUC) of 0.869 with a sensitivity of 43% at 95% specificity in the comparison of pCR and non-pCR LARC patients, which was significantly higher than AUC of each marker alone (VEGFR3: AUC = 0.720, *p* = 0.0152, likelihood ratio test; EGFR: AUC = 0.679, *p* = 0.0160, likelihood ratio test; COX2: AUC = 0.679, *p* = 0.0151, likelihood ratio test) in the pretreatment LARC set (Figure 3B).

#### *3.3. Correlation of VEGFR3, EGFR, and COX2 in the Plasma of Rectal Cancer Patients Before and After Surgery*

We next determined whether plasma levels of VEGFR3, EGFR, and COX2 are associated with surgical resection of rectal cancer. For this analysis, VEGFR3, EGFR, and COX2 were assayed in an independent set of plasma consisting of samples collected from 18 rectal cancer patients at the time of diagnosis and 5 to 7 months after surgical resection (pre-and post-surgery RC set; Figure 4A). All these rectal cancer patients remained disease-free for five years after surgery. Plasma levels of VEGFR3 and EGFR were significantly decreased after tumor resection (VEGFR3: *p* = 0.0119, EGFR: *p* = 0.0058, paired *t*-test) (Figure 4B). Levels of plasma COX2 did not change significantly after surgery compared to presurgical levels (*p* = 0.1867, paired *t*-test).

**Figure 4.** Correlation of plasma VEGFR3, EGFR, and COX2 with surgical resection in patients with rectal cancer. (**A**). Subject characteristics in the pre-and post-surgery RC set. (**B**). Levels of VEGFR3, EGFR, and COX2 in plasmas collected at the time of diagnosis (Before) and 5 to 7 months after surgery (After) from 18 rectal cancer patients. *p* values were calculated by paired *t*-tests.

88

#### **4. Discussion**

In this study, we profiled the pretreatment plasma proteome of a mouse model of rectal cancer treated with concurrent chemoradiation to identify potential blood-based biomarkers for predicting pCR in LARC patients. Among plasma protein signatures associated with Non-regression, proteins involved in focal adhesion are of particular interest, as deregulation of integrin-mediated focal adhesion has been shown to lead to therapeutic resistance [30]. We found that levels of Vegfr3, one of the proteins in the "Focal adhesion" pathway, was significantly increased in the pretreatment plasma of Non-regression mice compared to Regression mice and control mice. Increased levels of circulating VEGFR3 were validated in plasma collected prior to nCRT from non-pCR LARC patients compared to pCR LARC patients. We also demonstrated that plasma VEGFR3 levels were significantly decreased after surgical resection of rectal tumors. Yeh et al. recently showed a significant correlation between circulating VEGFR3 levels and expression of VEGFR3 in tumor tissues [31]. These findings suggest that circulating VEGFR3 emanated from tumor tissues. VEGFR3 is crucial for the development and maintenance of blood and lymphatic vascular systems [32]. While VEGFR3 is primarily expressed in lymphatic endothelial cells, VEGFR3 and its main ligand VEGF-C are expressed in tumor cells of various types of cancer, including colorectal cancer [33]. Higher expression of VEGFR3 in tumor tissues has been also associated with advanced TNM stages, the occurrence of metastasis, and poor prognosis in colorectal cancer [31,34]. Recent studies have revealed that activation of VEGF-C/VEGFR3 signaling promotes tumor growth and invasion by disrupting the lymphatic endothelial barrier and by recruiting and inducing immunosuppressive tumor-associated macrophages in colorectal cancer [34,35]. Given the emerging evidence suggesting crucial roles of VEGF-C/VEGFR3 axis in cancer progression, multiple therapeutic strategies for VEGF-C/VEGFR3-targeted therapies, including small molecule VEGFR3 inhibitors, monoclonal antibodies targeting VEGF-C, and neutralizing antibodies or peptides that block VEGFR3 signaling, have been developed [33]. Therefore, while our findings indicated the potential of circulating VEGFR3 as a biomarker for nCRT response, circulating VEGFR3 may also serve as a biomarker for the prediction of prognosis and response to VEGF-C/VEGFR3-targeted therapies.

In this study, we also explored whether tissue-based biomarkers that have been associated with nCRT response can serve as blood-based biomarkers for pCR prediction. Among four tissue-based biomarkers selected for testing, we observed significantly increased levels of circulating EGFR and COX2 in plasmas collected prior to nCRT from non-pCR LARC patients compared to pCR LARC patients.

Several studies have reported the significant association of tissue EGFR expression and response to nCRT in LARC [36]. A recent study indicated that higher expression of EGFR in the nucleus is associated with poor survival in LARC patients treated with nCRT [37]. While we demonstrated for the first time that circulating EGFR could be used as a potential biomarker for predicting pCR in rectal cancer, circulating EGFR has been associated with response to therapy in several types of cancer [38]. Interestingly, Okada et al. demonstrated that downregulation of EGFR in tumor tissue after treatment with anti-EGFR antibodies was significantly correlated with therapeutic response in patients with colorectal cancer [39]. Although a recent meta-analysis indicated the addition of EGFR inhibitors did not improve the efficacy of neoadjuvant therapy in KRAS-wild type LARC patients [40], monitoring circulating EGFR may help determine whether to continue EGFR-targeted therapy or not.

Regarding tissue COX2 expression in LARC, previous studies have reported an association of COX2 overexpression and poor response to nCRT [20]. While we demonstrated that circulating COX2 was significantly increased in the plasma of non-pCR LARC patients compared to pCR LARC patients, surgical resection of rectal tumors did not affect circulating COX2 levels. As COX2 is induced by various inflammation mediators [41], increased levels of circulating COX2 in LARC patients may be due to cancer-associated systemic inflammation or some other physiological stress, such as infection. However, given

that development of cancer-associated systemic inflammation is associated with a poorer outcome [42] and that a recent phase II clinical trial of nCRT combining COX2 inhibitor celecoxib in LARC improved efficacy and decreased toxicity [43], it would be interesting to determine whether circulating COX2 levels can predict prognosis and response to nCRT combined with COX2 inhibitors.

The logistic regression model combining circulating VEGFR3, EGFR, and COX2 yielded a significantly higher AUC in differentiating pCR and non-CR compared to that of each marker alone. However, blood contains a wide variety of measurable molecules and cellular materials, including exosomes, tumor-derived DNAs, microRNAs, autoantibodies, and metabolites, making blood a rich resource of biomarkers. Therefore, it is critical to determine the relevance and relative contributions of the different types of biomarkers in the same samples [20], allowing to further refine the biomarker panel with integrating other previously or newly identified biomarkers. In addition, due to a small sample size in the current study, the performance of these three blood-based biomarkers will need to be assessed in larger sample sets, and the biomarker panel will need to be further refined with integrating other previously validated biomarkers.

#### **5. Conclusions**

In conclusion, we identified circulating VEGFR3 as a novel biomarker for predicting pCR through proteomic analysis of plasmas from a mouse model of rectal cancer, and further confirmed increased VEGFR3 levels in pretreatment plasmas from non-pCR LARC patients compared to pCR LARC patients. We also demonstrated that levels of circulating EGFR and COX2, known tissue-based biomarkers for nCRT response, were significantly increased in pretreatment plasma of non-pCR LARC patients. Our findings provide a rationale for further studies to safely adopt the watch-and-wait strategy with using bloodbased biomarkers in LARC patients.

**Supplementary Materials:** The following are available online at https://www.mdpi.com/article/ 10.3390/cancers13143642/s1. Table S1: Significantly associated pathways for more than a two-fold increase of proteins in the plasma of pCR mice or non-pCR mice compared to control mice.

**Author Contributions:** Conceptualization, S.K. and A.T.; Data curation, H.W., H.K. and S.H.; Formal analysis, C.A.-B. and K.-A.D.; Investigation, D.D., J.G., D.C.W., G.P., Y.A., S.H., S.K. and A.T.; Methodology, D.D., D.C.W. and A.B.; Resources, J.G., Y.H., D.C.W., A.B., K.H., T.K., K.K., Y.S., M.T., Y.N., Y.A.W. and R.D.; Writing—Original Draft, D.D. and A.T.; Writing—review and editing, all authors. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was supported in part by the MD Anderson start-up funds, the Aichi Cancer Research Foundation, the Suzuken Memorial Foundation, the Daiwa Securities Health Foundation, and the JSPS KAKENHI Grant Number JP20K09116. K.A.D. was partially supported by a Cancer Center Support Grant NCI Grant P30 CA016672, NIH grants UL1TR003167 and 5R01GM122775, the prostate cancer SPORE P50 CA140388, CPRIT grant RP160693, and the Moon Shots funding at the MD Anderson Cancer Center.

**Institutional Review Board Statement:** This study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Institutional Review Board of the University of Texas MD Anderson Cancer Center (PA12-0614) and Aichi Cancer Center (2016-1-310).

**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.

**Data Availability Statement:** The results of the proteomic analysis of mouse plasmas presented in this study are available upon reasonable request from the corresponding author.

**Conflicts of Interest:** The authors declare no competing interests to disclose.

#### **References**


**2.6. Alignment and phylogeny**

Mega7.0.14 software was used for alignment (Clustal W algorithm with Blossom 62 matrix) and reconstruction of the phylogenetic pattern (Neighbor Joining model with JTT matrixbased model for distance computing and 1000 replicates as bootstrap test).

**Table 1.** Primers used in semi-quantitative PCR.

#### **2.7. Semi-quantitative RT-PCR**

The RNA from different seed stages (1, 4 and 8 months of development) digested with DNAse I amplification grade (Invitrogen) was used as a template in sqPCR reactions, and the synthesis of cDNAs was carried out with the First Strand cDNA Synthesis Kit (Thermo Scientific). In Veriti 96-well Thermal cycler (Applied Biosystems), in reactions of 10 μl (100 ng/μl cDNA,10X Reaction Buffer, 2 mM dNTPs, 50 mM MgCl2 , 1 U Taq DNA polymerase), sqPCR primers (**Table 1**) were designed by Primer3 webtool (http://bioinfo. ut.ee/primer3-0.4.0/); the primers for bHLH transcription factor, three metallothioneins, snakin and ubiquitin as reference gene were selected. The amplification procedures were 95°C - 10min., 30 cycles (95°C - 45 s, annealing 30 s, amplification 72°C - 45 s), 72°C - 7 min. Gene expression ratio between selected gene and endogenous control was calculated using band intensity measured with GelAnalyzer 2010. The semi-quantitative PCR was performed with three repeats.