**Loss of SATB2 Occurs More Frequently Than CDX2 Loss in Colorectal Carcinoma and Identifies Particularly Aggressive Cancers in High-Risk Subgroups**

**Maxime Schmitt 1,2,†, Miguel Silva 3,†, Björn Konukiewitz 2,4, Corinna Lang <sup>2</sup> , Katja Steiger <sup>2</sup> , Kathrin Halfter <sup>5</sup> , Jutta Engel <sup>5</sup> , Paul Jank <sup>1</sup> , Nicole Pfarr <sup>2</sup> , Dirk Wilhelm <sup>6</sup> , Sebastian Foersch <sup>7</sup> , Carsten Denkert <sup>1</sup> , Markus Tschurtschenthaler 3,8 , Wilko Weichert 2,9 and Moritz Jesinghaus 1,2,\***


**Simple Summary:** The immunohistochemical analysis of Special AT-rich sequence-binding protein 2 (SATB2) is increasingly being used to detect colorectal differentiation. Our study aimed to investigate SATB2 expression levels and the prognostic relevance of SATB2 loss in colorectal carcinoma (CRC), especially in comparison with CDX2, the standard marker of colorectal differentiation. We tested SATB2 expression in 1039 CRCs and identified SATB2 as a strong prognosticator in the overall cohort as well as in specific subcohorts, including high-risk subgroups. Compared to CDX2, SATB2 showed a higher prognostic power but was lost at a much higher frequency, generally rendering SATB2 as the less sensitive marker for colorectal differentiation compared to CDX2.

**Abstract:** Background: Special AT-rich sequence-binding protein 2 (SATB2) has emerged as an alternative immunohistochemical marker to CDX2 for colorectal differentiation. However, the distribution and prognostic relevance of SATB2 expression in colorectal carcinoma (CRC) have to be further elucidated. Methods: SATB2 expression was analysed in 1039 CRCs and correlated with clinicopathological and morphological factors, CDX2 expression as well as survival parameters within the overall cohort and in clinicopathological subgroups. Results: SATB2 loss was a strong prognosticator in univariate analyses of the overall cohort (*p* < 0.001 for all survival comparisons) and in numerous subcohorts including high-risk scenarios (UICC stage III/high tumour budding). SATB2 retained its prognostic relevance in multivariate analyses of these high-risk scenarios (e.g., UICC stage III: DSS: *p* = 0.007, HR: 1.95), but not in the overall cohort (DSS: *p* = 0.1, HR: 1.25). SATB2 loss was more frequent than CDX2 loss (22.2% vs. 10.2%, *p* < 0.001) and of higher prognostic relevance with only moderate overlap between SATB2/CDX2 expression groups. Conclusions: SATB2 loss is able to identify especially aggressive CRCs in high-risk subgroups. While SATB2 is the prognostically superior immunohistochemical parameter compared to CDX2 in univariate analyses, it appears to be the less sensitive marker for colorectal differentiation as it is lost more frequently.

**Citation:** Schmitt, M.; Silva, M.; Konukiewitz, B.; Lang, C.; Steiger, K.; Halfter, K.; Engel, J.; Jank, P.; Pfarr, N.; Wilhelm, D.; et al. Loss of SATB2 Occurs More Frequently Than CDX2 Loss in Colorectal Carcinoma and Identifies Particularly Aggressive Cancers in High-Risk Subgroups. *Cancers* **2021**, *13*, 6177. https:// doi.org/10.3390/cancers13246177

Academic Editors: Marta Baiocchi and Ann Zeuner

Received: 12 November 2021 Accepted: 5 December 2021 Published: 7 December 2021

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**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/).

**Keywords:** SATB2; colorectal carcinoma; prognosis; CDX2

#### **1. Introduction**

Considering that colorectal carcinoma (CRC) currently ranks among the three most common cancers in humans concerning incidence and mortality worldwide [1,2], further explorations on potentially relevant biomarkers are warranted in order to characterise these tumours precisely and improve prognostic predictions.

Special AT-rich sequence-binding protein 2 (SATB2), a transcription factor interacting with nuclear matrix attachment regions which is highly expressed in the non-neoplastic colorectal mucosa [3,4], attracted increasing scientific notice for the identification of the colorectal origin of cancers of unknown primary and of CRC metastases [5–10], delineating SATB2 as a valid addition to CDX2, which is still the most established immunohistochemical marker associated with colorectal differentiation [5,11].

Previous immunohistochemical assessments of SATB2 in CRC showed a general association of a diminished SATB2 expression with poorer survival characteristics and microsatellite status [12–16]. However, it remains unclear whether SATB2 is differently expressed within purely morphological (adenocarcinoma NOS vs. specific CRC subtypes, tumour budding subcategories (Bd1/2/3), WHO low- vs. high-grade carcinomas), immunohistochemical (CDX2 expression) and pTNM/UICC stage subgroups. Furthermore, it remains to be elucidated, how frequently the loss of SATB2 occurs in comparison to the loss of CDX2 and whether SATB2 can identify distinct prognostic groups within these colorectal cancer subsets.

To address these questions, we investigated SATB2 expression in a large cohort comprising 1039 CRCs and correlated the results with histomorphologic and immunohistochemical (CRC subtypes, tumour budding activity, WHO grade, CDX2 expression) as well as clinicopathological parameters (pTNM/UICC staging, tumour localisation) and explored the prognostic relevance of SATB2 expression in uni- and multivariate survival analyses in the overall cohort as well as in specific subgroups.

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

#### *2.1. Study Population*

Our study cohort comprised one thousand and thirty-nine CRC patients that underwent surgical resection between 1997 and 2019 at the University Hospital Klinikum rechts der Isar of the Technical University of Munich, Germany. Only patients with colorectal carcinomas were included in this study. Patients suffering from other colorectal tumours (e.g., neuroendocrine tumours, non-epithelial tumours, etc.), insufficient tissue on the constructed tissue microarray or incomplete clinicopathological/survival data were excluded. The original cohort (1997–2018) was recently extended with cases from 2019 (*n* = 36) [17]. Clinicopathological characteristics as well as survival data were extracted from hospital records or from the Munich Cancer Registry. Definitions of survival parameters, survival endpoints and general treatment modalities were defined as described previously [18,19]. The local ethic committee of the Technical University of Munich approved this study (reference number: 252/16 s).

#### *2.2. Evaluation of SATB2 Expression and Clinicopathological Parameters*

SATB2 expression was analysed by SATB2 immunohistochemistry in 1039 CRCs on a tissue microarray with two tumour-carrying cores from each tumour. We used an automated immunostainer (BOND RXm System, Leica Biosystems, Germany) for the immunohistochemical staining of SATB2 (EP281, Cellmarque 384R, Ready-To-Use, Cell Marque, USA), which is the standard SATB2 antibody used in daily clinicopathological routine and which has been used by various previous studies [5,11,15,20]. We stained 2 µm thick sections from our tissue microarray. Antigen retrieval was performed with

Epitope Retrieval 1 after deparaffinisation, (Leica Biosystems, Germany; equivalent to citrate buffer pH 6) for 20 min and antibody binding was detected using a Polymer Refine Detection Kit (Leica Biosystems, Nußloch, Germany) without a postprimary antibody and haematoxylin counterstain. Naturally, pretested positive/negative control-tissues were stained in parallel. Two independent observers (MJ, MS (Maxime Schmitt)) that were blinded to clinicopathological parameters manually performed the evaluation of SATB2 expression.

We assessed the number of positive carcinoma cells for each individual patient. Counting a minimum of 500 tumour cells, the resulting cumulative percentage score for both cores was then assigned for each CRC (range: 0–100%). Only a nuclear staining of SATB2 was considered specific. SATB2 expression patterns (combined from both cores) were classified into three separate groups: diffuse, if the tumours either showed a complete expression or only a very focal loss in singular cells; heterogeneous, if areas with a complete loss of staining were observed; absent, if the tumours were completely negative. A strong staining intensity was defined as an intensity comparable to normal colonic mucosa, a still easily identifiable but slightly weaker staining was rated as medium. A barely visible staining intensity was classified as weak. Cases without any detectable staining were classified as absent.

SATB2-grouping results from the TMA were compared with SATB2 staining from 20 randomly selected full block slides, interobserver variability was probed in 150 cases that were assessed by the two observers in a blinded fashion.

SATB2 expression was correlated with clinicopathological variables including staging data and with Haematoxylin and Eosin-based histopathological parameters defined by the recent WHO classification (CRC subtypes: Adenocarcinoma NOS, Mucinous adenocarcinoma, Signet-ring carcinoma, Medullary carcinoma, Serrated adenocarcinoma, Micropapillary adenocarcinoma, Adenoma-like adenocarcinoma, Adenosquamous carcinoma, Carcinoma with sarcomatoid components, Undifferentiated carcinoma, MANEC/NEC; WHO grade: low grade, formerly G1/G2 vs. high grade, formerly G3 and tumour budding: Bd1 (0-4 Buds in 20×), Bd2 (5-9 Buds in 20×), Bd3 (≥10 Buds in 20×)). The parameters were available from a previous study on the same cohort regarding the distribution and the prevalence of the essential morphologic criteria given in the 2019 WHO classification of colorectal carcinoma, from which also the microsatellite status was extracted [17] (cohort details; Table 1). Furthermore, SATB2 expression was correlated with CDX2 expression, which was analysed in a previous study [18], where a similar methodology regarding the finding of an optimised cutoff for CDX2 expression groups was used [21]. The cases from 2019 that were recently added to the collective (*n* = 36) were classified regarding the aforementioned parameters (histomorphology, CDX2 expression) as described previously [17,18].




**Table 1.** *Cont.*

#### *2.3. Statistics*

Using SPSS version 26 (SPSS Institute, Chicago, IL, USA) statistical analyses were performed applying X<sup>2</sup> test as well as X<sup>2</sup> test for trends and Fisher's exact test. The Cutoff Finder, a publicly available biostatistical tool that represents a bundle of optimisation and visualisation methods for cutoff determination, was used to define the optimal cutoffs for SATB2 expression groups [21]. Where applicable, the Bonferroni method was used to correct for multiple testing. Univariate survival analyses were performed using the Kaplan–Meier method and significance of survival differences was tested by a log-rank test. The Cox proportional hazard model was used for multivariate analyses. All statistical tests were performed two-sided, *p*-values ≤ 0.05 were considered significant.

#### **3. Results**

#### *3.1. Clinicopathological Features and Survival*

The median patient age was 69 years. The majority of patients were male (*n* = 599; 58%). Left- (descending colon until rectum; *n* = 536; 52%) and right-sided (coecum until splenic flexure; *n* = 503; 48%) neoplasms showed an almost even distribution. Postoperative UICC staging (eighth edition of the TNM classification of malignant tumours) [22] resulted in 213 stage I (21%), 350 (34%) stage II, 318 (31%) stage III and 158 (15%) stage IV cancers. Three hundred and thirty patients (32%) relapsed, 411 patients (40%) died during follow up, for 301 (29%) patients a tumour-specific death was noted (cohort details: Table 1).

3.1.1. Distribution of SATB2 Expression and Biostatistical Generation of SATB2 Expression Groups

Most CRCs showed a diffuse SATB2 expression (61%, *n* = 639), a heterogeneous staining was noted for 340 cancers (33%), 60 tumours (6%) showed a complete absence of SATB2. A nuclear staining in ≥90% of tumour cells was observed in 65% (679/1039) of cases. In order to transform this continuous variable into dichotomous SATB2 groups (binary variable), we used the Cutoff Finder [21], a publicly available biostatistical tool for cutoff determination, to identify the best cutoff for SATB2 stratification. Following these initial statistical analyses, two SATB2-groups were formed: CRCs that showed an SATB2 expression above the 20th percentile (>70% tumour cells; *n* = 808, 78%) were categorised as SATB2-high, CRC on/below the 20th percentile (range:0–70% of tumour cells; *n* = 231, 22%) were categorised as SATB2-low/absent. Examples of the two SATB2 expression groups among certain CRC subtypes are given in Figure 1. SATB2-low/absent CRCs usually showed a reduced SATB2 staining intensity and a significantly higher rate of a heterogeneous/absent staining pattern (*p* < 0.001, details see Table S1). Only the number of positive tumour cells (regardless of staining pattern or intensity) were used to form the SATB2 expression groups. A comparison of the results of the SATB2-grouping with full block slides showed an excellent concordance with the results from the TMA (95%, *p* < 0.001, Kappa Cohens value: 0.88). Furthermore, an excellent interobserver variance was evident (*p* < 0.001, Kappa Cohens value: 0.95). *Cancers* **2021**, *13*, x FOR PEER REVIEW 6 of 15

**Figure 1.** SATB2-low/absent and SATB2-high expression groups in selected colorectal carcinoma subtypes. (**A**–**D**): Adenocarcinoma NOS from the SATB2-high ((A): HE, 20× + (B): SATB2, 20×) and from association the SATB2-low/absent ((C): HE, 20× + (D): SATB2, 20×) expression subgroup. (**E**–**H**)**:** Micropapillary adenocarcinoma from the SATB2-high ((E): HE, **Figure 1.** SATB2-low/absent and SATB2-high expression groups in selected colorectal carcinoma subtypes. (**A**–**D**): Adenocarcinoma NOS from the SATB2-high ((**A**): HE, 20× + (**B**): SATB2, 20×) and from association the SATB2-low/absent

3.1.2. Association of SATB2-Groups with pTNM/UICC Staging, Morphologic Parameters

As illustrated in Figure 2 and depicted in detail in Table S2, SATB2-low/absent CRCs were significantly enriched in higher pT/pN/pM and combined UICC-stages, right-sided tumours, carcinomas with lymphatic and blood vessel invasion as well as in tumours with positive margins (*p* < 0.001, respectively). Compared to SATB2-high neoplasms, SATB2 low/absent CRCs were significantly increased in CRCs with high (Bd3) tumour budding activity and in poorly differentiated carcinomas according to the WHO grade (*p* < 0.001, respectively). Furthermore, a low/absent SATB2 expression was significantly enriched in the mucinous, micropapillary, medullary and signet-ring CRC subtypes as well as in MANEC/NEC (*p* < 0.001). MSI-H CRCs were also associated with an absent or low SATB2

Far more CRCs were allocated to the SATB2 low/absent expression group than to the CDX2 low/absent expression group (CDX2: 10.2% vs. SATB2: 22.2%). Although both expression groups were associated with one another (*p* < 0.001), there were many tumours with a discordant SATB2/CDX2 expression status (Kappa Cohens value: 0.30). For example, only 28% (64/231) of CRCs from the SATB2-low/absent subgroup showed a concordant low/absent CDX2 expression, while 40% (42/106) of CDX2-low/absent CRCs showed a high SATB2 expression level. Sixty CRCs (6%) showed a complete absence of SATB2 expression compared to only 13 CRCs (1.3%) that showed a complete negativity for CDX2. Only two CRCs remained negative for both markers, while the rest of the CRCs without any SATB2 expression showed a heterogeneous (*n* = 19, 32%) or diffuse expression of

(CRC Subtypes/Tumour Budding/WHO Grade) and Microsatellite Status

3.1.3. Association of SATB2 Expression Groups with CDX2 Expression Groups

SATB2, 20×) and from association the SATB2-low/absent ((O): HE, 20× + (P): SATB2, 20×) expression subgroup.

expression (*p* = 0.01).

((**C**): HE, 20× + (**D**): SATB2, 20×) expression subgroup. (**E**–**H**)**:** Micropapillary adenocarcinoma from the SATB2-high ((**E**): HE, 20× + (**F**): SATB2, 20×) and from association the SATB2-low/absent ((**G**: HE, 20× + (**H**: SATB2, 20×) expression subgroup, also shown as an example of carcinomas with a high tumour budding activity from both expression groups. (**I**–**L**): Mucinous adenocarcinoma from the SATB2-high ((**I**): HE, 20× + (**J**): SATB2, 20×) and from association the SATB2-low/absent ((**K**): HE, 20× + (**L**): SATB2, 20×) expression subgroup. (**M**–**P**): Medullary carcinoma from the SATB2-high ((**M**): HE, 20× + (**N**): SATB2, 20×) and from association the SATB2-low/absent ((**O**): HE, 20× + (**P**): SATB2, 20×) expression subgroup.

#### 3.1.2. Association of SATB2-Groups with pTNM/UICC Staging, Morphologic Parameters (CRC Subtypes/Tumour Budding/WHO Grade) and Microsatellite Status

As illustrated in Figure 2 and depicted in detail in Table S2, SATB2-low/absent CRCs were significantly enriched in higher pT/pN/pM and combined UICC-stages, right-sided tumours, carcinomas with lymphatic and blood vessel invasion as well as in tumours with positive margins (*p* < 0.001, respectively). Compared to SATB2-high neoplasms, SATB2 low/absent CRCs were significantly increased in CRCs with high (Bd3) tumour budding activity and in poorly differentiated carcinomas according to the WHO grade (*p* < 0.001, respectively). Furthermore, a low/absent SATB2 expression was significantly enriched in the mucinous, micropapillary, medullary and signet-ring CRC subtypes as well as in MANEC/NEC (*p* < 0.001). MSI-H CRCs were also associated with an absent or low SATB2 expression (*p* = 0.01).

#### 3.1.3. Association of SATB2 Expression Groups with CDX2 Expression Groups

Far more CRCs were allocated to the SATB2 low/absent expression group than to the CDX2 low/absent expression group (CDX2: 10.2% vs. SATB2: 22.2%). Although both expression groups were associated with one another (*p* < 0.001), there were many tumours with a discordant SATB2/CDX2 expression status (Kappa Cohens value: 0.30). For example, only 28% (64/231) of CRCs from the SATB2-low/absent subgroup showed a concordant low/absent CDX2 expression, while 40% (42/106) of CDX2-low/absent CRCs showed a high SATB2 expression level. Sixty CRCs (6%) showed a complete absence of SATB2 expression compared to only 13 CRCs (1.3%) that showed a complete negativity for CDX2. Only two CRCs remained negative for both markers, while the rest of the CRCs without any SATB2 expression showed a heterogeneous (*n* = 19, 32%) or diffuse expression of CDX2 (*n*= 39, 65%). Of the 13 completely CDX2 negative cases, the vast majority (*n* = 11, 85%) showed a heterogenous or diffuse SATB2 expression (details: Table 2).


**Table 2.** Correlation of SATB2 and CDX2 expression groups and CDX2/SATB2 staining patterns.

CDX2 (*n*= 39, 65%). Of the 13 completely CDX2 negative cases, the vast majority (*n* = 11,

85%) showed a heterogenous or diffuse SATB2 expression (details: Table 2).

**Figure 2.** (**A**–**D**): Prevalence of SATB2 expression groups within the overall cohort (**A**) and in pT (**B**), pN (**C**), pM (**D**) and combined UICC stage subgroups (**E**). (**F**–**G**): Distribution of SATB2 expression groups among MSS vs. MSI-H (**F**) and CDX2-low/absent vs. CDX2-high CRCs (**G**). (**H**–**K**): Differential distribution of SATB2 within WHO low-grade vs. WHO high-grade CRCs (**H**), among the different tumour budding subgroups (Bd1, Bd2, Bd3; (**I**)), among colorectal cancer subtypes (**J**) and in right- vs. left-sided CRCs (**K**). **Figure 2.** (**A**–**D**): Prevalence of SATB2 expression groups within the overall cohort (**A**) and in pT (**B**), pN (**C**), pM (**D**) and combined UICC stage subgroups (**E**). (**F**,**G**): Distribution of SATB2 expression groups among MSS vs. MSI-H (**F**) and CDX2-low/absent vs. CDX2-high CRCs (**G**). (**H**–**K**): Differential distribution of SATB2 within WHO low-grade vs. WHO high-grade CRCs (**H**), among the different tumour budding subgroups (Bd1, Bd2, Bd3; (**I**)), among colorectal cancer subtypes (**J**) and in right- vs. left-sided CRCs (**K**).

#### *3.2. Prognostic Relevance of SATB2-Groups in the Overall Cohort*

As illustrated in Figure 3 and Table 1, compared to SATB2-high CRCs, the SATB2 low/absent group showed a significantly decreased OS (SATB2-high 82.2 months vs. SATB2-low/absent 68.4 months, *p* < 0.001), DSS (SATB2-high 91.5 months vs. SATB2-

low/absent 74.2 months, *p* < 0.001) and DFS (SATB2-high 86.3 months vs. SATB2-low/absent 68 months, *p* < 0.001) in univariate analyses (log-rank test) of the overall cohort of 1039 CRCs. CRCs (e.g., DSS: SATB2-high 92.1 months vs. SATB2-low/absent 68.9 months, *p* < 0.001) (Figure S1).

months, *p* = 0.012; DFS: 62.1 months vs. 81.2 months; *p* = 0.004) (Table S4) in all survival

SATB2 expression showed a strong prognostic impact in right-sided (e.g., DSS: SATB2-high 90.8 months vs. SATB2-low/absent 77.4 months, *p* = 0.002) and left-sided

comparisons. In UICC stage I/II and IV, no significant survival impact was visible.

*Cancers* **2021**, *13*, x FOR PEER REVIEW 9 of 15

**Figure 3.** Prognostic relevance of SATB2 expression in univariate analyses on overall-, disease specific- and disease free-survival in the overall cohort (**A**–**C**) and for disease-specific survival in **Figure 3.** Prognostic relevance of SATB2 expression in univariate analyses on overall-, disease specificand disease free-survival in the overall cohort (**A**–**C**) and for disease-specific survival in specific CRC subgroups: MSS (**D**) and MSI-H (**E**) subcohorts, high tumour budding activity subcohort (**F**), WHO high-grade (**G**)/WHO low-grade (**H**) subcohorts, CDX2-low/absent (**I**)/CDX2-high (**J**) subcohorts.

#### 3.2.1. Prognostic Relevance of SATB2 in Microsatellite and CDX2 Expression Subgroups

A strong prognostic impact of the SATB2-low/absent group on OS/DSS/DFS was present in MSS CRCs (65.4 months/70 months/62.8 months, *p* < 0.001, respectively). SATB2 was also prognostic for DSS (SATB2-high 105.8 months vs. SATB2-low/absent 90.6 months, *p* = 0.023), but not for OS (SATB2-high 92.3 months vs. SATB2-low/absent 78.8 months, *p* = 0.072) or DFS (SATB2-high 101 months vs. SATB2-low/absent 89 months, *p* = 0.163) in MSI-H CRC.

When analysed in CDX2 expression subgroups, SATB2 expression groups showed a strong prognostic demarcation in both CDX2-low/absent (e.g., DSS: SATB2-high 91.2 months vs. SATB2-low/absent 64.5 months, *p* = 0.021) as well as in CDX2 high CRCs (e.g., DSS: SATB2-high 91.6 months vs. SATB2-low/absent 77.3 months, *p* < 0.001) (Figure 3, Table S3), while CDX2 expression showed no prognostic relevance in any of the SATB2 expression groups (e.g., *p* > 0.05, data not shown).

#### 3.2.2. Prognostic Relevance of SATB2 in WHO Grade and Tumour Budding Subgroups

SATB2 expression showed a strong prognostic impact in WHO low-grade (e.g., DSS: SATB2-high 96.7 months vs. SATB2-low/absent 88.1 months, *p* = 0.027) and high-grade CRCs (e.g., DSS: SATB2-high 78.8 months vs. SATB2-low/absent 59.5 months, *p* = 0.002).

When analysed within the different tumour budding subgroups (Bd1/2/3), SATB2 showed a weak prognostic relevance in tumours with a low tumour budding activity (e.g., DSS: SATB2-high 110.2 months vs. SATB2-low/absent 104.4 months *p* = 0.05) and especially a strong prognostic impact within CRCs with a high tumour budding activity, where CRCs with a low/absent SATB2 expression showed a significantly worse survival rate compared to SATB2-high carcinomas (e.g., DSS: SATB2-high 51.1 months vs. SATB2 low/absent 29 months, *p* = 0.002) (Figure 3, Table S3).

3.2.3. Prognostic Relevance of SATB2 in UICC Stage Subgroups and Right vs. Left-Sided CRCs

In UICC stage III tumours, SATB2-low/absent showed significantly shortened survival characteristics (OS: 70.3 months vs. 84.4 months, *p* = 0.025; DSS: 74.5 months vs. 91 months, *p* = 0.012; DFS: 62.1 months vs. 81.2 months; *p* = 0.004) (Table S4) in all survival comparisons. In UICC stage I/II and IV, no significant survival impact was visible.

SATB2 expression showed a strong prognostic impact in right-sided (e.g., DSS: SATB2 high 90.8 months vs. SATB2-low/absent 77.4 months, *p* = 0.002) and left-sided CRCs (e.g., DSS: SATB2-high 92.1 months vs. SATB2-low/absent 68.9 months, *p* < 0.001) (Figure S1).

#### *3.3. Multivariate Analyses*

In multivariate analyses (including age, gender, resection status, UICC stage, MSIstatus, WHO grade, tumour budding, CRC subtypes and SATB2-groups) SATB2-expression was not an independent prognostic factor (e.g., DSS: *p* = 0.1, hazard ratio: 1.25, Table S5) in the overall cohort comprising all CRCs. When the other main histological confounders (WHO grade, tumour budding, CRC subtypes) were excluded from the Cox-regression analysis, SATB2-expression remained a prognostic factor independent of UICC stage, age, gender, resection status and MSI-status (DSS: 0.029, HR:1.32, Table S6).

In a full multivariate analyses (including all parameters mentioned above) of high risk CRC subcohorts (UICC stage III/high tumour budding activity), SATB2 fully retained its prognostic relevance demonstrated in univariate analyses (UICC stage III CRC subcohort: DSS: *p* = 0.007, hazard ratio: 1.95, Table 3; Bd3-CRCs with a high tumour budding activity DSS: *p* = 0.01, hazard ratio: 1.67, Table 4; DFS: *p* = 0.02, Hazard Ratio: 1.79; OS: *p* = 0.01, hazard ratio: 1.58, data not shown).


**Table 3.** Multivariate disease-specific survival analysis in the UICC stage III subcohort under inclusion of SATB2 expression, age, gender, CRC subtype, tumour budding, WHO grade, resection status and microsatellite status.

**Table 4.** Multivariate disease-specific survival analysis in the high tumour budding (Bd3) subcohort under inclusion of SATB2 expression, age, gender, CRC subtype, UICC stage, WHO grade, resection status and microsatellite status.



**Table 4.** *Cont.*

#### **4. Discussion**

In this study, we investigated the expression of Special AT-rich sequence-binding protein 2 (SATB2) in one thousand and thirty-nine resected CRCs, correlated the results with histomorphologic parameters (CRC subtypes, tumour budding activity, WHO grade) [17], expression of CDX2 [18] as well as clinicopathological parameters (pTNM/UICC staging, microsatellite status, localisation) and analysed the prognostic relevance of SATB2 in the overall cohort and in specific subcohorts.

In recent years, SATB2 has gained increasing attention as a relatively specific marker of colorectal differentiation [23–26] and functional studies have revealed the tumoursuppressive properties of SATB2 in experimental settings [12,27–30], demonstrating that SATB2 is a complexly regulated tumour suppressor that represses CRC progression by inhibiting the transcription of SNAIL, a master regulator of epithelial-mesenchymal transition. In our cohort of more than one thousand tumours, SATB2 low/absent CRCs were significantly associated with higher UICC stages and massively enriched in tumours with high-risk histomorphological features such as high tumour budding (Bd3) or poorly differentiated carcinomas according to the WHO grade, which is in line with the functional studies postulating the tumour suppressive properties of SATB2 [12,27,28]. Consistent with previous findings, SATB2-low/absent CRCs were also associated with strongly reduced survival parameters in univariate analyses (log-rank test) of the overall cohort. Interestingly, although SATB2 expression retained its statistical significance in multivariate analyses when UICC stage, resection status, MSI-status, age and gender were incorporated, this independent prognostic power vanished when we added the most common histomorphological parameters of CRC, tumour budding, WHO grade and the different CRC subtypes to our multivariate analyses. These findings argue towards the fact that the massive enrichment of SATB2 low expressing tumours in high-grade categories of these parameters probably washes out the strong prognostic effect of SATB2 that is present in univariate analyses.

Nevertheless, we wanted to know whether SATB2 can identify any prognostic subgroup that is not identified by either UICC staging or classical histomorphological parameters and which is also retained in multivariate analyses incorporating all factors.

Interestingly, SATB2 low/absent CRCs were associated with an especially aggressive disease course in CRCs with high tumour budding activity and in UICC stage III cancers and showed highly reduced survival times in these high-risk subgroups. In subsequent multivariate analyses incorporating all prognostic factors, SATB2 retained its prognostic relevance in both UICC stage III carcinomas and dissociative cancers with a high tumour budding activity. These findings delineate SATB2 as a worthwhile immunohistochemical biomarker in CRC that can identify especially aggressive cancers in these high-risk subgroups of CRC and delivers valuable additional prognostic information in addition to standard histomorphological factors and UICC staging. Regarding the translation of these results into clinicopathological routine, we suggest that the responsible pathologist reports SATB2 loss by stating the percentage of SATB2 expressing cancer cells, ideally combined with the notion that a significantly reduced expression of SATB2 has been associated with a poorer clinical outcome.

As SATB2 has emerged as a considerable alternative to CDX2 to verify or rule out colorectal differentiation, another aim of our study was to compare the incidence and overlap of SATB2 loss with loss of CDX2 and also to compare the prognostic relevance of these two markers with one another [18]. The findings of these aspects of our study are also particularly interesting, because they highlight possible strengths and weaknesses of the most commonly used colorectal markers. Notably, the frequency of a reduced or completely lost SATB2 expression is much higher compared to CDX2 and especially a completely absent SATB2 expression was far more frequent than a complete negativity for CDX2. This implies, that when both CDX2 and SATB2 (as singular markers) are assessed regarding their ability to detect a colorectal origin in neoplastic tissues, SATB2 has to be ranked as the less sensitive marker compared to CDX2. However, as only two out of 1039 CRCs (0.2%) showed a complete loss of both SATB2 and CDX2, and the majority of SATB2 negative CRCs showed a strong and diffuse expression of CDX2 (and vice versa), a combined panel of both markers appears to be able to identify the overwhelming majority of colorectal cancers and is probably the most expedient approach for routine diagnostic settings.

When we then moved on to compare the prognostic impact of SATB2 and CDX2, we observed an opposing picture. Compared to CDX2, the loss of SATB2 showed a considerably higher prognostic impact in univariate analyses (log-rank test) of the overall cohort and in nearly all clinicopathological subscenarios of CRC, in which CDX2 mostly showed at best minimal prognostic impact in our cohort. A crucial difference between the prognostic difference of SATB2 and CDX2 was also that SATB2 retained its prognostic power in right- and left-sided CRCs, while CDX2 did not show any prognostic significance in right-sided tumours although it is more frequently lost in the right colon [18]. In line with these findings, we additionally observed that SATB2-low/absent CRCs were able to identify patients with a poor prognosis in both CDX2 expression groups (CDX2-low/absent vs. CDX2- high), while CDX2 showed no prognostic relevance in SATB2 expression subgroups, rendering SATB2 as the prognostically superior immunohistochemical biomarker in CRC compared to CDX2.

#### **5. Conclusions**

Our study has five major findings: (1) a low/absent SATB2 expression is significantly enriched in advanced stage CRCs that have an aggressive histomorphological phenotype with high tumour budding activity and/or a poor differentiation according to the WHO grade. (2) Loss of SATB2 is of high prognostic relevance in uni- and multivariate analyses (including UICC stage) in the overall cohort, but shows no independent prognostic value in the overall cohort when the main histomorphological parameters of CRC (tumour budding, WHO grade, CRC subtypes) are added to the multivariate analyses. (3) SATB2 shows an especially high prognostic relevance in uni- and multivariate analyses of high-risk clinicopathological subgroups (high tumour budding/UICC stage III) and identifies CRCs with a particularly aggressive disease course in these high-risk scenarios. (4) SATB2 loss

occurs much more frequently than loss of CDX2, with a substantial portion of SATB2 negative CRCs showing a diffuse or at least heterogeneous CDX2 positivity, generally delineating CDX2 as the more sensitive marker of colorectal differentiation in carcinomas. (5) SATB2, in general, showed a vastly better prediction of survival outcome compared to CDX2, with SATB2 retaining its prognostic impact in CDX2 expression subgroups (CDX2 low/absent vs. high), rendering SATB2 as the superior prognostic biomarker compared to CDX2.

In conclusion, our study identifies SATB2 as a potentially valuable additional prognostic biomarker in CRC. Further studies are warranted to explore the possible therapeutic implications of a diminished or completely lost SATB2 expression. Both SATB2 and CDX2 can individually be completely lost in CRCs, while a total absence of both markers is almost never observed. Therefore, a combined panel of both markers appears to be the most solid approach to pinpoint or rule out colorectal differentiation.

**Supplementary Materials:** The following are available online at https://www.mdpi.com/article/ 10.3390/cancers13246177/s1. Figure S1: Prognostic relevance of SATB2 expression for diseasespecific survival in right-sided (A, coecum until splenic flexure) and left-sided (descending colon until rectum) CRCs, Table S1: Correlation between SATB2 expression groups with SATB2 staining pattern (A) and SATB2 staining intensity (B), Table S2: Distribution of SATB2 expression groups with clinicopathological and morphological parameters in the overall cohort, Table S3: Impact of SATB2 expression on overall, disease-specific and disease-free survival in tumour budding—(Bd1, Bd2, Bd3), WHO grade (low, high), microsatellite (MSS, MSI-H) and CDX2 expression subcohorts (low/absent, high), Table S4: Impact of SATB2 expression on overall, disease-specific and disease-free survival in the different UICC stage groups, Table S5: Multivariate overall survival analysis in the overall cohort under inclusion of SATB2 expression, age, gender, CRC subtype, tumour budding, WHO grade and microsatellite status, Table S6: Multivariate overall survival analysis in the overall cohort under exclusion of CRC subtype, tumour budding and WHO grade.

**Author Contributions:** M.J. designed this study, M.J., M.S. (Miguel Silva) and M.S. (Maxime Schmitt) wrote the manuscript with assistance from C.D., B.K., S.F., P.J., D.W., W.W., M.J., B.K. and M.S. (Maxime Schmitt) performed histopathological analyses. M.J., C.D., S.F., W.W., P.J., M.S. (Miguel Silva) and M.T. performed statistical analyses. M.J., K.S., C.L., N.P., D.W., K.H., J.E. collected clinicopathological data. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by grants of the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation): Project-ID 360372040—SFB 1335 to WW and Project-ID 39535707— SFB 1371 to KS and MT. The authors have disclosed that they have no significant relationships with, or financial interest in, any commercial companies pertaining to this article.

**Institutional Review Board Statement:** The study was conducted according to the guidelines of the Declaration of Helsinki. The local ethic committee of the Technical University of Munich approved this study (reference number: 252/16 s).

**Informed Consent Statement:** Patients signed a general informed consent protocol during admission to the hospital.

**Data Availability Statement:** All relevant data are within the paper and its supporting information files. The data underlying the results presented in the study are available from the study group upon reasonable request, some restrictions apply due to confidentiality of patient data. Since these data are derived from a research trial with ongoing follow up there are legal and ethical restrictions to share sensitive patient related data publicly. Data can be requested in the context of a translational research project by sending a request to the corresponding author.

**Acknowledgments:** The authors thank the Comparative Experimental Pathology Unit of the Institute of Pathology and especially Marion Mielke and Olga Seelbach for excellent technical assistance. Furthermore, we thank Simone Köppel and Christina Schott from the Biobank of the Klinikum rechts der Isar (MTBio) for their excellent support.

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

#### **References**


## *Article* **The Targeting of MRE11 or RAD51 Sensitizes Colorectal Cancer Stem Cells to CHK1 Inhibition**

**Luca Mattiello 1,2,† , Sara Soliman Abdel Rehim 1,3,†, Martina Musella 4,†, Antonella Sistigu 4,5 , Andrea Guarracino 1,3 , Sara Vitale <sup>4</sup> , Francesca Corradi <sup>3</sup> , Claudia Galassi <sup>4</sup> , Francesca Sperati <sup>6</sup> , Gwenola Manic 1,2,\* ,‡ , Ruggero De Maria 4,7,\* ,‡ and Ilio Vitale 1,2,\* ,‡**

	- <sup>3</sup> Department of Biology, University of Rome "Tor Vergata", 00133 Rome, Italy; francesca.corradi89@gmail.com
	- <sup>4</sup> Dipartimento di Medicina e Chirurgia Traslazionale, Università Cattolica del Sacro Cuore, 00168 Rome, Italy; martina.musella@unicatt.it (M.M.); antonella.sistigu@unicatt.it (A.S.); sara.vitale@unicatt.it (S.V.); claudia.galassi@unicatt.it (C.G.)
	- <sup>5</sup> UOSD Tumor Immunology and Immunotherapy Unit, IRCCS Regina Elena National Cancer Institute, 00144 Rome, Italy
	- <sup>6</sup> UOSD Biostatistics, Bioinformatics and Clinical Trial Center, San Gallicano Dermatological Institute IRCCS, 00144 Rome, Italy; francesca.sperati@ifo.gov.it
	- <sup>7</sup> Fondazione Policlinico Universitario "A. Gemelli"—IRCCS, 00168 Rome, Italy
	- **\*** Correspondence: gwenola.manic@gmail.com or manic.esterni@iigm.it (G.M.);
	- ruggero.demaria@unicatt.it (R.D.M.); iliovit@gmail.com or ilio.vitale@iigm.it (I.V.)
	- † Shared first authorship.
	- ‡ Shared senior co-authorship.

**Simple Summary:** The ATR-CHK1 axis of the DNA damage response is crucial for the survival of most colorectal cancer stem cells (CRC-SCs), but a significant fraction of primary CRC-SCs either is resistant to ATR or CHK1 inhibitors or survives the abrogation of the ATR-CHK1 cascade despite an initial response. Here, we demonstrate that the targeting of RAD51 or MRE11 improves the sensitivity of primary CRC-SCs to the CHK1/2 inhibitor prexasertib by sequentially inducing replication stress, the abrogation of cell cycle checkpoints, and the emergence of mitotic defects. This results in the induction of mitotic catastrophe and CRC-SC killing via a caspase-dependent apoptosis.

**Abstract:** Cancer stem cells (CSCs) drive not only tumor initiation and expansion, but also therapeutic resistance and tumor relapse. Therefore, CSC eradication is required for effective cancer therapy. In preclinical models, CSCs demonstrated high capability to tolerate even extensive genotoxic stress, including replication stress, because they are endowed with a very robust DNA damage response (DDR). This favors the survival of DNA-damaged CSCs instead of their inhibition via apoptosis or senescence. The DDR represents a unique CSC vulnerability, but the abrogation of the DDR through the inhibition of the ATR-CHK1 axis is effective only against some subtypes of CSCs, and resistance often emerges. Here, we analyzed the impact of druggable DDR players in the response of patientderived colorectal CSCs (CRC-SCs) to CHK1/2 inhibitor prexasertib, identifying RAD51 and MRE11 as sensitizing targets enhancing prexasertib efficacy. We showed that combined inhibition of RAD51 and CHK1 (via B02+prexasertib) or MRE11 and CHK1 (via mirin+prexasertib) kills CSCs by affecting multiple genoprotective processes. In more detail, these two prexasertib-based regimens promote CSC eradication through a sequential mechanism involving the induction of elevated replication stress in a context in which cell cycle checkpoints usually activated during the replication stress response are abrogated. This leads to uncontrolled proliferation and premature entry into mitosis of replication-stressed cells, followed by the induction of mitotic catastrophe. CRC-SCs subjected to RAD51+CHK1 inhibitors or MRE11+CHK1 inhibitors are eventually eliminated, and CRC-SC tumorspheres inhibited or disaggregated, via a caspase-dependent apoptosis. These results support further clinical development of these prexasertib-based regimens in colorectal cancer patients.

**Citation:** Mattiello, L.;

Soliman Abdel Rehim, S.; Musella, M.; Sistigu, A.; Guarracino, A.; Vitale, S.; Corradi, F.; Galassi, C.; Sperati, F.; Manic, G.; et al. The Targeting of MRE11 or RAD51 Sensitizes Colorectal Cancer Stem Cells to CHK1 Inhibition. *Cancers* **2021**, *13*, 1957. https://doi.org/10.3390/ cancers13081957

Academic Editor: David Wong

Received: 15 March 2021 Accepted: 11 April 2021 Published: 19 April 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/).

**Keywords:** colorectal cancer; chromosomal instability; DNA damage; targeted therapy; tumor-initiating cells

#### **1. Introduction**

Robust experimental evidence indicates that human tumors often exhibit a hierarchical organization, whose apex is occupied by a subpopulation of cancer cells known as cancer stem cells (CSCs) because of the ability to self-renew and generate a progeny with different degree of differentiation (reviewed in [1]). CSCs have been identified and prospectively isolated from colorectal and other cancers, where they are believed to promote tumor development and to contribute to disease expansion, evolution and dissemination [2–6]; for these reasons, they are also known as tumor-initiating cells or tumor-propagating cells. In addition, CSCs have been shown to act as a main source of tumor heterogeneity [7], which is in turn linked to dismal prognosis and therapy resistance, as well as of tumor relapse [1,8]. At least in part, these features are linked to the relative low proneness of CSCs to undergo regulated cell death under stress conditions. In particular, in preclinical patient-derived models, CSCs have demonstrated elevated resistance to DNA damages, making them able to tolerate constitutive replication stress–defined as the slowing or stalling of replication fork progression and/or DNA synthesis [9]–or survive conventional genotoxic agents, including ionizing radiation and chemotherapeutics (reviewed in [10]).

The current view is that the resistance of CSCs derives from the orientation of the DNA damage response (DDR) towards cytoprotection. The DDR is a multipronged mechanism specifically activated in cells experiencing DNA lesions, operating through a two-step sensing-signal transduction cascade. This culminates either in the activation of cytoprotective signaling modules favoring the repair of or tolerance to DNA lesions, or in the activation of cytotoxic signaling modules leading to proliferation arrest or demise of irreversibly damaged cells upon induction of cell senescence or regulated cell death [10,11]. The activation of these pathways depends on the severity of the insults as well as on the efficiency of the machineries responsible for sensing, repairing, or tolerating DNA lesions [10]. Other crucial factors orienting the DDR towards cytosurvival or cytotoxicity encompass the proficiency, robustness, and activation kinetics of cytoprotective versus cytosurvival pathways. Collectively, these features dictate the outcome (i.e., survival or death) of DNA-damaged cells.

There is evidence that CSCs survive extensive DNA damage because of their capability not only to tolerate and repair DNA lesion, but also to detoxify reactive oxygen species and/or extrude DNA-damaging drugs [12–14]. Other factors tipping the balance toward the survival of CSC experiencing extensive DNA lesions include: (i) limited activation (or silencing) of cytotoxic mechanisms, which is due to the intrinsic deregulation of apoptotic pathways and evasion of regulated cell death or senescence [15,16], together with (ii) high proneness to activate cytosurvival cascades, originating from the constitutive activation of the DDR signaling [17–19]. In particular, a basal overactivaton of the ataxia telangiectasia mutated serine/threonine kinase (ATM)-checkpoint kinase 2 (CHEK2, best known as CHK2) axis, which is primarily activated by double-strand breaks (DSBs), or the ataxia telangiectasia mutated and Rad3 related serine/threonine kinase (ATR)-checkpoint kinase 1 (CHEK1, best known as CHK1) axis, which is primarily activated by long stretches of single-stranded DNA (ssDNA) adjacent to double-stranded (ds)/ssDNA junctions during replication stress, has been observed in multiple experimental models of CSC enrichment, including patient-derived models [20–22].

Importantly, the presence of a robust DDR constitutes a vulnerability of CSCs. On the one hand, the targeting of these DDR kinases in combination with DNA damaging agents de facto silences the cytosurvival modules of the DDR, thus reestablishing tumor sensitivity to DNA damage. On the other hand, CSCs can be particularly dependent on DDR kinase(s) for survival even in the absence of exogenous sources of DNA damage, making them

targetable by the abrogation of the DDR signaling. Therefore, the DDR can be harnessed to revert the intrinsic capacity of CSCs to evade apoptosis. Accordingly, inhibitors of the DDR kinase ATM, ATR and CHK1 are reported to kill CSCs when administered either alone [20,23] or in combination with other DNA damaging agents to which CSCs were formerly resistant [17,19,24,25].

However, one emerging concept is that the response of CSCs to inhibitors of DDR kinases is heterogeneous, restricting the therapeutic use of these drugs to specific subtypes. Although most colorectal CSCs (CRC-SCs) are sensitive to inhibitors of the ATR-CHK1 cascade ultimately succumbing via replication catastrophe subsequent to the induction of intolerable or lethal levels of replication stress, a significant fraction of CSCs survives this regimen [20]. As a further limitation of these strategies, we recently revealed a mechanism of resistance to ATR-CHK1 inhibitors in CRC-SCs based on upregulation of poly(ADPribose) polymerase 1 (PARP1) [26], a DDR player with pleiotropic roles in DNA damage repair, the response to replication stress, and regulated cell death [27].

Based on these considerations, in this study, we investigated the impact of relevant druggable DDR players on the survival of patient-derived CRC-SCs, identifying MRE11 homolog, double-strand break repair nuclease (MRE11) and RAD51 recombinase (RAD51) as targets for sensitizing CRC-SCs to CHK1 inhibitors.

#### **2. Results**

#### *2.1. Identification of RAD51 and MRE11 as Sensitizing Targets to Enhance the Efficacy of the CHK1 Inhibitor Prexasertib in CRC-SCs*

We recently established distinct pairs of primary CRC-SCs sensitive (SENS) and resistant (RES) to pharmacological inhibitors of ATR and CHK1 kinases [26], the principal transducers of the cellular response to replication stress. From this panel, we selected two pairs of CRC-SCs: #1SENS/#1RES and #19SENS/#19RES, and used them to identify novel actionable targets improving the sensitivity and/or reverting the resistance to CHK1 inhibitors. We first analyzed the impact of crucial druggable DDR players on RES-CRC-SC survival. To this aim, RES-CRC-SCs were treated or not with the CHK1 inhibitor prexasertib together with inhibitors of ATR (VE-821), ATM (KU-60019), DNA-PK (NU-7026), MRE11 (mirin), and RAD51 (B02). After (co)treatments, cells were assessed for their proliferation/survival with a CellTiter-Glo® Luminescent Cell Viability assay. As internal comparison, RES-CRC-SCs were exposed to two representative pharmacological agents we previously demonstrated to potently sensitize to ATR/CHK1 inhibitors: triapine and adavosertib, which inhibit, respectively, the ribonucleotide reductase regulatory subunit M2 (RRM2) and WEE1 G2 checkpoint kinase (WEE1). This analysis led to the identification of the inhibitors of MRE11 (mirin) and of RAD51 (B02) as novel sensitizers of CRC-SCs to prexasertib (Figure 1A, Table S1). As opposed to mirin and B02, pharmacological inhibitors of the DDR kinases ATM, ATR and DNA-PK were ineffective or mildly effective in sensitizing CRC-SCs to prexasertib (Figure 1A, Table S1). This result rules out redundant or parallel roles of DDR kinases in the replication stress of CSCs, also excluding a potential contribution of ATR in resolving non-stringent RS independently of CHK1, as previously reported [28].

In following dose-response studies, we confirmed that the inhibition of RAD51 or MRE11 mildly affected the survival of RES-CRC-SCs when administered alone (Figure 1B,C). On the contrary, both mirin and B02 sensitized RES-CRC-SCs to CHK1 inhibitors (Figure 1B,C), thus validating our previous result. Along similar lines, B02 or mirin administered as monotherapies had minimal effect also on the survival of SENS-CRC-SCs (Figure 1D) and on that of CRC-SCs of our panel intrinsically resistant to CHK1 inhibitors (innRES-CRC-SCs) (Figure 1E). However, these agents increased the responsiveness of all these cells to CHK1 inhibition. Indeed, the inhibition of RAD51 or of MRE11 promoted sensitization of SENS-CRC-SCs (Figure 1D) and of innRES-CRC-SCs (Figure 1E) to sublethal doses of prexasertib. In conclusion, these findings demonstrate that the targeting of MRE11 or RAD51 sensitizes CRC-SCs to the inhibition of CHK1.

**Figure 1.** Identification of RAD51 and MRE11 inhibitors as prexasertib-sensitizing agents in CRC-SCs**.** (**A**) CRC-SCs previously characterized as resistant to ATR-CHK1 abrogation (RES-CRC-SCs) were left untreated or treated for 72 h with the CHK1/2 inhibitor prexasertib (CHK1i), and/or a set of modulators of the DNA damage response (DDR) with known prexasertib-sensitizing effect (i.e., adavosertib and triapine) or with unknown impact on prexasertib CSC toxicity (i.e., B02, KU-60019, mirin, NU7026, and VE-821), as indicated. Cell proliferation and viability were assessed by CellTiter-Glo® assay. The heatmap shows prexasertib-sensitizing effects of DDR modulators, with values corresponding to the percentage of viable cells upon normalization on control conditions. Data are means of three independent experiments, with values reported in Supplementary Table S1. The heatmap and clusterization were generated with Python. (**B**,**C**) Cell proliferation/viability (assessed by CellTiter-Glo® assay) of distinct RES-CRC-SCs left untreated or exposed for 96 h to CHK1i alone or in combination with RAD51i (B02) (**B**) or MRE11i (mirin) (**C**), as indicated. Results are means±SEM and individual data points of six (RAD51i-treated #19RES), five (RAD51i-treated #1RES), four (MRE11i-treated #1RES), or three (MRE11itreated #19RES) independent experiments. \**p* < 0.05, \*\**p* < 0.01, \*\*\**p* < 0.001 (one-way ANOVA and Bonferroni or Dunnett's T3 post-hoc test) as reported. (**D**,**E**) Cell proliferation/viability (evaluated by CellTiter-Glo® assay) of representative CRC-SCs sensitive to CHK1i (SENS-CRC-SCs) (**D**) or intrinsically resistant to CHK1i (innRES-CRC-SCs) (**E**) left untreated or subjected for 96 h to CHK1i alone or in combination with MRE11i or RAD51i, as indicated. Results are means ± SEM and individual data points of four (MRE11i-treated SENS-CRC-SCs and #innRES) or three (RAD511i-treated SENS-CRC-SCs) independent experiments. \**p* < 0.05, \*\**p* < 0.01, \*\*\**p* < 0.001 (one-way ANOVA and Bonferroni or Dunnett's T3 post-hoc test) as reported. Associated supplementary table: Supplementary Table S1. In following dose-response studies, we confirmed that the inhibition of RAD51 or **Figure 1.** Identification of RAD51 and MRE11 inhibitors as prexasertib-sensitizing agents in CRC-SCs. (**A**) CRC-SCs previously characterized as resistant to ATR-CHK1 abrogation (RES-CRC-SCs) were left untreated or treated for 72 h with the CHK1/2 inhibitor prexasertib (CHK1i), and/or a set of modulators of the DNA damage response (DDR) with known prexasertib-sensitizing effect (i.e., adavosertib and triapine) or with unknown impact on prexasertib CSC toxicity (i.e., B02, KU-60019, mirin, NU7026, and VE-821), as indicated. Cell proliferation and viability were assessed by CellTiter-Glo® assay. The heatmap shows prexasertib-sensitizing effects of DDR modulators, with values corresponding to the percentage of viable cells upon normalization on control conditions. Data are means of three independent experiments, with values reported in Supplementary Table S1. The heatmap and clusterization were generated with Python. (**B**,**C**) Cell proliferation/viability (assessed by CellTiter-Glo® assay) of distinct RES-CRC-SCs left untreated or exposed for 96 h to CHK1i alone or in combination with RAD51i (B02) (**B**) or MRE11i (mirin) (**C**), as indicated. Results are means±SEM and individual data points of six (RAD51i-treated #19RES), five (RAD51i-treated #1RES), four (MRE11i-treated #1RES), or three (MRE11i-treated #19RES) independent experiments. \* *p* < 0.05, \*\* *p* < 0.01, \*\*\* *p* < 0.001 (one-way ANOVA and Bonferroni or Dunnett's T3 post-hoc test) as reported. (**D**,**E**) Cell proliferation/viability (evaluated by CellTiter-Glo® assay) of representative CRC-SCs sensitive to CHK1i (SENS-CRC-SCs) (**D**) or intrinsically resistant to CHK1i (innRES-CRC-SCs) (**E**) left untreated or subjected for 96 h to CHK1i alone or in combination with MRE11i or RAD51i, as indicated. Results are means ± SEM and individual data points of four (MRE11i-treated SENS-CRC-SCs and #innRES) or three (RAD511i-treated SENS-CRC-SCs) independent experiments. \* *p* < 0.05, \*\* *p* < 0.01, \*\*\* *p* < 0.001 (one-way ANOVA and Bonferroni or Dunnett's T3 post-hoc test) as reported. Associated supplementary table: Supplementary Table S1.

MRE11 mildly affected the survival of RES-CRC-SCs when administered alone (Figure

#### *2.2. The Targeting of RAD51 or MRE11 Sensitize to CHK1 Inhibitors by Boosting Replication Stress*

We then analyzed the impact of combined inhibition of CHK1 and RAD51 (by prexasertib+B02) and CHK1 and MRE11 (by prexasertib+mirin) on DNA replication and DNA damage in CRC-SCs. Through western-blot studies, we provided evidence that these two prexasertib-based regimens promoted an elevated increase in the phosphorylation of RPA32 (pRPA32) (Figure 2A), a marker of ongoing replication stress [20,26,29], as compared to prexasertib alone. As expected, replication stress was not augmented by the administration of mirin or B02 alone (Figure 2A; Figure S1). Flow cytometry and fluorescence microscopy studies confirmed the induction of extensive DNA damage upon prexasertib-based combinatorial regimens in RES-CRC-SCs. Indeed, combined inhibition of CHK1+RAD51 and of CHK1+MRE11 significantly promoted the phosphorylation of H2AX (Figure 2B,C), a post-translational modification sensing DNA lesions best known as γH2AX [30]. In line with the induction of replication stress, in RES-CRC-SCs the two prexasertib-based combinations significantly increased the percentage of S-phase cells positive to γH2AX (Figure 2B), a marker of fork breakage occurring during elevated replication stress. Moreover, fluorescence microscopy studies revealed that a significant percentage of CRC-SCs cotreated either with CHK1+RAD51 inhibitors or with CHK1+MRE11 inhibitors displayed a diffuse γH2AX nuclear staining instead of classical nuclear foci, covering either all or a portion of the nuclei (Figure 2C). Such diffuse nuclear staining confirms the presence of excessive RS, indicating the induction of replication catastrophe.

Collectively, these findings indicate that the targeting of RAD51 or MRE11 sensitizes to CHK1 inhibitors by boosting replication stress to lethal levels.

#### *2.3. The Administration of Prexasertib Together with Mirin or B02 Alters Mitotic Timing*

We then performed an extensive flow cytometry-mediated characterization of cell cycle progression. We provided evidence that elevated replication stress induced by MRE11+CHK1 inhibitors in RES-CRC-SCs was accompanied by changes in cell cycle profiles, manifested with a significant accumulation of cells with a DNA content between 2*n* and 4*n* (presumably S-phase cells) (Figure 3A,B). A similar accumulation (though to a lesser extent) was observed in RES-CRC-SCs treated with CHK1+RAD51 inhibitors, while prexasertib, mirin and B02 monotherapies did not significantly affect cell cycle progression (Figure 3A,B). To enter more in-depth into such an effect, we extended cell cycle analysis focusing on phosphorylated histone 3 (pH3), a marker of mitosis. By cytofluorimetry, we observed that, when combined with prexasertib, mirin and B02 significantly increased the mitotic (pH3<sup>+</sup> ) fraction (Figure 3A,C). Intriguingly, upon exposure to prexasertib+B02 or prexasertib+mirin around 30% of cell positive for pH3<sup>+</sup> did not present the classic 4*n* DNA content, but bore a DNA content between 2*n* and 4*n* (Figure 3A,C). The presence of such a high number of pH3<sup>+</sup> cells with a DNA content lower than 4*n* indicates premature entry into mitosis upon cotreatment. This evidence demonstrates that combined inhibitions of CHK1 and RAD51 and of CHK1 and MRE11 in RES-CRC-SCs not only affect DNA replication but also deregulate cell cycle proliferation and mitotic timing. In particular, it suggests that prexasertib-based combinations push RES-CRC-SCs with unreplicated and/or damaged DNA into aberrant mitosis rather than causing an S-phase blockade. Again, this effect was absent or less evident in cells treated with prexasertib, B02 or mirin alone (Figure 3A–C). To further confirm this result, we performed a biparametric analysis of pH3 and γH2AX, observing a significant augmentation of the percentage of cells with double positivity for pH3 and γH2AX in RES-CRC-SCs subjected to prexasertibbased cotreatments (Figure 3D). Finally, by fluorescence microscopy studies we observed that, upon prexasertib+B02 or prexasertib+mirin cotreatment, almost half of the analyzed (pro)metaphases showed γH2AX foci (Figure 3E). This evidence confirms the presence of DNA damage in mitosis.

Altogether, these findings indicate that the induction of replication stress by combined inhibition of CHK1 and RAD51 or of CHK1 and MRE11 culminates in premature

mitosis entry and aberrant mitotic execution, thus supporting the occurrence of mitotic catastrophe [31]. *Cancers* **2021**, *13*, x FOR PEER REVIEW 6 of 20

**Figure 2.** Combined inhibition of CHK1 and MRE11 or CHK1 and RAD51 induces replication stress in prexasertib-resistant CRC-SCs. (**A**) Western-blot analysis of representative RES-CRC-SCs left untreated or administrated for 24 h with prexasertib (CHK1i), either alone or in combination with MRE11i (mirin) or RAD51i (B02), and then stained with antibodies recognizing phospho(p)RPA32 (S4/S8) and RPA32 (as markers of replication stress, RS) and nucleolin (to ensure equal lane loading). One representative western-blot and the quantification of the ratio pRPA32/nucleolin are shown (see also Supplementary Figure S1). Results are expressed as means±SEM and individual data points of five independent experiments.\**p* < 0.05, \*\**p* < 0.01, \*\*\**p* < 0.001 (Kruskal-Wallis test and Dunn's post-hoc test) compared to untreated conditions. (**B**) Flow cytometry analysis in representative RES-CRC-SCs left untreated or treated with CHK1i, either alone or combined with MRE11i or RAD51i, and then stained with a DNA intercalant (DAPI) together with an anti-γH2AX antibody. Cell cycle profiles and quantitative data (means ± SEM; six independent experiments at 24 h and seven independent experiments at 48 h) are reported. In cell cycle profiles, cells positive for γH2AX in S-phase are in green. Numbers indicate the percentage of corresponding events. In the histograms, the percentage of γH2AX<sup>+</sup> cells in all cell cycle phases (total) are in dark color, while the percentage of γH2AX<sup>+</sup> cells in S-phase are in pale color. \**p* < 0.05, \*\**p* < 0.01, \*\*\**p* < 0.001 (Kruskal-Wallis test and Dunn's post-hoc test), as indicated (for γH2AX<sup>+</sup> cells in all cell cycle phases). #*p* < 0.05, ##*p* < 0.01, ###*p* < 0.001 (Kruskal-Wallis test and Dunn's post-hoc test), as indicated (for γH2AX<sup>+</sup> cells in S-phase). (**C**) Immunofluorescence analysis in representative RES-CRC-SCs left untreated or exposed for 24 h to CHK1i, either alone or in combination with MRE11i or RAD51i, and then strained with an antibody recognizing γH2AX. Representative images and quantification of percentages of γH2AX<sup>+</sup> cells presenting "focal", "partially diffuse" or "pan-nuclear" γH2AX positivity are shown. For more information about the category of γH2AX positivity, see Materials and Methods. Data are expressed as means±SEM and individual data points of five independent experiments. \**p* < 0.05, \*\**p* < 0.01, \*\*\**p* < 0.001 (Kruskal-Wallis test and Dunn's post-hoc test) compared to untreated conditions (for all γH2AX<sup>+</sup> cells); #*p* < 0.05, ##*p* < 0.01, ###*p* < 0.001 (Kruskal-**Figure 2.** Combined inhibition of CHK1 and MRE11 or CHK1 and RAD51 induces replication stress in prexasertib-resistant CRC-SCs. (**A**) Western-blot analysis of representative RES-CRC-SCs left untreated or administrated for 24 h with prexasertib (CHK1i), either alone or in combination with MRE11i (mirin) or RAD51i (B02), and then stained with antibodies recognizing phospho(p)RPA32 (S4/S8) and RPA32 (as markers of replication stress, RS) and nucleolin (to ensure equal lane loading). One representative western-blot and the quantification of the ratio pRPA32/nucleolin are shown (see also Supplementary Figure S1). Results are expressed as means±SEM and individual data points of five independent experiments. \* *p* < 0.05, \*\* *p* < 0.01, \*\*\* *p* < 0.001 (Kruskal-Wallis test and Dunn's post-hoc test) compared to untreated conditions. (**B**) Flow cytometry analysis in representative RES-CRC-SCs left untreated or treated with CHK1i, either alone or combined with MRE11i or RAD51i, and then stained with a DNA intercalant (DAPI) together with an anti-γH2AX antibody. Cell cycle profiles and quantitative data (means ± SEM; six independent experiments at 24 h and seven independent experiments at 48 h) are reported. In cell cycle profiles, cells positive for γH2AX in S-phase are in green. Numbers indicate the percentage of corresponding events. In the histograms, the percentage of γH2AX<sup>+</sup> cells in all cell cycle phases (total) are in dark color, while the percentage of γH2AX<sup>+</sup> cells in S-phase are in pale color. \* *p* < 0.05, \*\* *p* < 0.01, \*\*\* *p* < 0.001 (Kruskal-Wallis test and Dunn's post-hoc test), as indicated (for γH2AX<sup>+</sup> cells in all cell cycle phases). # *p* < 0.05, ## *p* < 0.01, ### *p* < 0.001 (Kruskal-Wallis test and Dunn's post-hoc test), as indicated (for γH2AX<sup>+</sup> cells in S-phase). (**C**) Immunofluorescence analysis in representative RES-CRC-SCs left untreated or exposed for 24 h to CHK1i, either alone or in combination with MRE11i or RAD51i, and then strained with an antibody recognizing γH2AX. Representative images and quantification of percentages of γH2AX<sup>+</sup> cells presenting "focal", "partially diffuse" or "pan-nuclear" γH2AX positivity are shown. For more information about the category of γH2AX positivity, see Materials and Methods. Data are expressed as means±SEM and individual

data points of five independent experiments. \* *p* < 0.05, \*\* *p* < 0.01, \*\*\* *p* < 0.001 (Kruskal-Wallis test and Dunn's post-hoc test) compared to untreated conditions (for all γH2AX<sup>+</sup> cells); # *p* < 0.05, ## *p* < 0.01, ### *p* < 0.001 (Kruskal-Wallis test and Dunn's post-hoc test) compared to untreated conditions (for each distinct category of γH2AX positivity, depicted with the indicated color code). Dose range in (**A**–**C**): 100 nM CHK1i, 20 µM MRE11i for #19RES or 30 µM MRE11i for #1RES, 7.5 µM RAD51i; a.u., arbitrary units. Associated supplementary figure: Supplementary Figure S1. *Cancers* **2021**, *13*, x FOR PEER REVIEW 8 of 20

**Figure 3.** Combined inhibition of MRE11 and CHK1 or of RAD51 and CHK1 causes an accumulation of premature and damaged mitoses in CRC-SCs resistant to prexasertib. (**A**–**D**) Cytofluorimetric assessment of cell cycle profiles (**A**,**B**) or the levels of phospho(p)H3 (S10) (**A**,**C**) and/or pH3 and γH2AX (**D**) in representative RES-CRC-SC left untreated or treated for 24 h with prexasertib (CHK1i) alone or in combination with MRE11i (mirin) or RAD51i (B02), and then stained with DAPI and the appropriates antibodies. Cell cycle profiles and quantitative data (means±SEM from six independent experiments) are reported. In **A**, cells positive for pH3 in S-phase and G2/M-phase are in orange and red, respectively. Numbers indicate the percentage of corresponding events. In **C**, the percentage of premature mitosis corresponds to the percentage of pH3<sup>+</sup> cells with a DNA content lower than 4*n* among all pH3<sup>+</sup> cells, while the percentage of normal mitoses corresponds **Figure 3.** Combined inhibition of MRE11 and CHK1 or of RAD51 and CHK1 causes an accumulation of premature and damaged mitoses in CRC-SCs resistant to prexasertib. (**A**–**D**) Cytofluorimetric assessment of cell cycle profiles (**A**,**B**) or the levels of phospho(p)H3 (S10) (**A**,**C**) and/or pH3 and γH2AX (**D**) in representative RES-CRC-SC left untreated or treated for 24 h with prexasertib (CHK1i) alone or in combination with MRE11i (mirin) or RAD51i (B02), and then stained with DAPI and the appropriates antibodies. Cell cycle profiles and quantitative data (means ± SEM from six independent experiments) are reported. In (**A**), cells positive for pH3 in S-phase and G2/M-phase are in orange and red, respectively. Numbers indicate the percentage of corresponding events. In (**C**), the percentage of premature mitosis corresponds to the percentage of pH3<sup>+</sup> cells with a DNA content lower than 4*n* among all pH3<sup>+</sup> cells, while the percentage of normal mitoses corresponds to the percentage of pH3<sup>+</sup> cells with a 4*n* DNA content among all cells. \* *p* < 0.05, \*\* *p* < 0.01, \*\*\* *p* < 0.001 (one-way ANOVA and Bonferroni or Dunnett's T3 post-hoc test in (**B**), in (**C**) for the analysis of the percentages of pH3<sup>+</sup> cells and of premature

mitoses, and in (**D**); Kruskal-Wallis test and Dunn's post-hoc test in (**C**), for the analysis of the percentages of mitoses), as indicated. (**E**) Immunofluorescence detection of DNA damage in mitosis in RES-CRC-SCs left untreated or exposed for 24 h to CHK1i, alone or in combination with MRE11i or RAD51i, and then strained with DAPI and an antibody recognizing γH2AX. The panel shows representative images of (pro)metaphases (as demonstrated by the classical chromosome condensation in gray scale images) with DNA damage foci. Green numbers refer to numbers of analyzed mitoses (n) and the percentages of γH2AX<sup>+</sup> mitoses pooled from five independent experiments. Dose range in (**A**–**E**): 100 nM CHK1i, 20 µM MRE11i for #19RES or 30 µM MRE11i for #1RES, 7.5 µM RAD51i; a.u., arbitrary units.

#### *2.4. Mitotic Catastrophe by CHK1-Based Regimens Is Executed via Caspase-Dependent Apoptosis*

We then explored the mechanism of cell killing by prexasertib+mirin and prexasertib+B02. Through western-blot, we observed similar constitutive levels of MRE11 and RAD51 in both SENS- and RES-CRC-SCs (Figure 4A; Figure S2). Moreover, no significant modulation of the level of these proteins was detected upon administration of CHK1 inhibitor, alone or in combination with MRE11 and/or RAD51 inhibitors (Figure 4B; Figure S3), ruling out a mechanism of sensitization mediated by the upregulation of MRE11 and/or RAD51. We then investigated whether the demise of RES-CRC-SCs undergoing mitotic catastrophe occurred via apoptosis, focusing on the involvement of caspases. Immunofluorescence microscopy studies of caspase 3 (CASP3) activation revealed that both prexasertib+B02 and prexasertib+mirin induced a significant increase in the level of cleaved CASP3 (Figure 4C), the activated form of this protease during apoptosis. On the contrary, monotherapies with these drugs did not significantly activate CASP3 (Figure 4C). Similar findings were obtained through flow cytometry analyses. In these studies, we observed a global increase of CASP3 activation in CRC-SCs subjected to prexasertib-based regimens, and in particular of CASP3A<sup>+</sup> cells displaying a DNA content between 2*n* and 4*n* (which include premature mitoses) and a 4*n* DNA content (which include apparent normal mitoses) (Figure 4D). In line with CASP3 involvement, combined inhibitions of CHK1 with RAD51 or CHK1 with MRE11 induced the cleavage of PARP1 (Figure 4E; Figure S4), a downstream target of activated CASP3. Consistently, PARP1 cleavage was completely abolished in CRC-SCs subjected to these prexasertib-based combinations by the administration of the caspase inhibitor Q-VD-Oph (Figure 4E; Figure S4).

Collectively, these results indicate that combined inhibition of CHK1 and MRE11 or RAD51 ultimately kills CRC-SCs via caspase-dependent apoptosis.

#### *2.5. Prexasertib in Combination with RAD51 or MRE11 Inhibitors Disrupts 3D Tumorsphere Organization and Growth*

We then analyzed the impact of the coinhibition of CHK1 and RAD51 or MRE11 on the organization and survival of CRC-SCs grown in vitro as 3D tumorspheres. We first performed live fluorescence microscopy in RES-CRC-SCs left untreated or administered with CHK1, RAD51 and/or MRE11 inhibitors, either alone or in combination. After treatment, RES-CRC-SCs were co-incubated with SYTOX, a vital dye incorporated by cells undergoing regulated cell death due to loss of membrane integrity, together with Hoechst 33342 to visualize the nuclei (Figure 5A). Image analysis of SYTOX incorporation confirmed that combined treatment of prexasertib with either B02 or mirin induced a high level of apoptosis in tumorspheres as compared to prexasertib, B02 or mirin monotherapies (Figure 5A). This evidence demonstrates the occurrence of apoptosis in CRC-SC tumorspheres. To explore more in-depth this phenomenon, we performed live videomicroscopy studies monitoring RES-CRC-SCs grown as spheres for approximately 67 h upon drug administration. In this analysis, we provided evidence that the two prexasertib-based combinations affected 3D tumorsphere growth and organization. Indeed, prexasertib+mirin (and to a lesser extent) prexasertib+B02 induced disaggregation of a significant number of spheres, manifested with sphere demise or dissolution (Figure 5B). Moreover, both combinations (and in particular prexasertib+B02) decelerated the growth of viable spheres (i.e., those that did not undergo disaggregation) as shown by the mild increase in sphere diameter at the end of the experimentation in these conditions, a phenomenon also observed with

prexasertib (Figure 5B). Further confirming the effect on sphere organization, combined administration of prexasertib with B02 or mirin (but not or less monotherapies) promoted the occurrence of multiple rounds of expulsion of cells/spheres displaying an apoptotic morphology (Figure 5B).

Collectively, these results demonstrate that mirin and B02 sensitize CRC-SCs to prexasertib by disrupting tumorsphere organization and growth. *Cancers* **2021**, *13*, x FOR PEER REVIEW 10 of 20

**Figure 4.** The inhibition of MRE11 or RAD51 sensitizes CRC-SCs to prexasertib by inducing a caspase-dependent mitotic catastrophe. (**A**,**B**) Western-blot analysis of MRE11 and RAD51 levels in SENS-CRC-SCs (**A**) and RES-CRC-SCs (**A**, **B**) left untreated or administrated with prexasertib (CHK1i), alone (**A**,**B**) or in combination with MRE11i (mirin) or RAD51i (B02) (**B**) (as indicated), and then stained with the appropriates antibodies. In **B**, two independent experiments for #1RES (<sup>1</sup> and <sup>2</sup>) are shown. Cofilin, nucleolin and β-tubulin were used to ensure equal lane loading. Representative western-blots are reported. Quantifications are shown in Supplementary Figures S2 and S3. (**C**,**D**) Immunofluorescence- and flow cytometry-mediated detection of the activation of caspase 3 (CASP3A) in RES-CRC-SCs upon treatment for 24 h (**C**) or 48 h (**D**) with CHK1i, MRE11i, and/or RAD51i as indicated, followed by co-staining with the DNA intercalant Hoechst (**C**) or DAPI (**D**) and an anti-CASP3a antibody. In **C**, representative images and data quantification are shown. In **D**, cell cycle profiles (left) and quantification of the percentage of CASP3A<sup>+</sup> cells among all cells (center) and the relative percentage of CASP3A<sup>+</sup> cells in G1-, S-, G2/M-phase (right) are illustrated. In cell cycle profiles, positivity for CASP3A in G1-, S-, G2/M-phase is depicted in pale blue, violet and dark blue, respectively. Numbers indicate the percentages of corresponding events. In **Figure 4.** The inhibition of MRE11 or RAD51 sensitizes CRC-SCs to prexasertib by inducing a caspase-dependent mitotic catastrophe. (**A**,**B**) Western-blot analysis of MRE11 and RAD51 levels in SENS-CRC-SCs (**A**) and RES-CRC-SCs (**A**,**B**) left untreated or administrated with prexasertib (CHK1i), alone (**A**,**B**) or in combination with MRE11i (mirin) or RAD51i (B02) (**B**) (as indicated), and then stained with the appropriates antibodies. In (**B**), two independent experiments for #1RES ( <sup>1</sup> and <sup>2</sup> ) are shown. Cofilin, nucleolin and β-tubulin were used to ensure equal lane loading. Representative western-blots are reported. Quantifications are shown in Supplementary Figures S2 and S3. (**C**,**D**) Immunofluorescence- and flow cytometry-mediated detection of the activation of caspase 3 (CASP3A) in RES-CRC-SCs upon treatment for 24 h (**C**) or 48 h (**D**) with CHK1i, MRE11i, and/or RAD51i as indicated, followed by co-staining with the DNA intercalant Hoechst (**C**) or DAPI (**D**) and an anti-CASP3a antibody. In (**C**), representative images and data quantification are shown. In (**D**), cell cycle profiles (left) and quantification of the percentage of CASP3A<sup>+</sup> cells among all cells (center) and the relative percentage of CASP3A<sup>+</sup> cells in G<sup>1</sup> -, S-, G2/M-phase (right) are illustrated. In cell cycle profiles, positivity for CASP3A in G<sup>1</sup> -, S-, G2/M-phase is depicted in pale blue, violet and dark blue, respectively. Numbers indicate the percentages of corresponding events. In the histograms, results are expressed as means ± SEM from three or seven independent experiments in (**C**,**D**),

respectively. \* *p* < 0.05, \*\* *p* < 0.01, \*\*\* *p* < 0.001 (one-way ANOVA and Bonferroni post-hoc test) compared to untreated conditions. (**E**) Western-blot analysis in RES-CRC-SCs treated for 48 h as indicated, using an antibody directed against PARP1, also recognizing the cleaved (c) form. β-Actin was used to monitor equal lane loading. Representative western-blots are shown (see also Supplementary Figure S4). Quantification of data, expressed as means ± SEM, and individual data points are from three independent experiments. cPARP1, cleaved PARP1. Dose range in **A**–**E**: 100 nM CHK1i, 20 µM MRE11i for #19RES or 30 µM MRE11i for #1RES, 15 µM Q-VD-Oph, 7.5 µM RAD51i; a.u., arbitrary units. Associated supplementary figures: Supplementary Figures S2–S4. *Cancers* **2021**, *13*, x FOR PEER REVIEW 12 of 20

**Figure 5.** The cooperation between MRE11+CHK1 and RAD51+CHK1 is essential for the survival of CRC-SCs resistant to prexasertib. (**A**) Live cell microscopy assessment of apoptosis induction in representative RES-CRC-SCs left untreated or

exposed for 48 h to prexasertib (CHK1i), MRE11i (mirin) and/or RAD51i (B02) as indicated, and then incubated for 10 min with SYTOX (which incorporates only dead cells) and the DNA dye Hoechst. SYTOX incorporation (as a parameter of regulated cell death activation) was evaluated by live fluorescence microscopy and image analysis. Nuclear fluorescence was used to discriminate spheres (area higher than 1000 pixels) from single cells or small cell aggregates (area lower than 1000 pixels) on the basis of the threshold indicated with a red arrow point (1000 pixels). Spheres were further classified in small spheres (area comprised between 1000 and 3000 pixels) and big spheres (area > 3000 pixels) on the basis of the threshold indicated with a yellow arrow point (approximately 3000 pixels). Green intensity means (i.e., SYTOX incorporation) were quantified in such spheres. Data, expressed as ratios of SYTOX/Hoechst intensity, are a pool of two independent experiments performed on distinct RES-CRC-SCs, and are shown as box-plots with means and individual data points. In the box-plot on the right, spheres are divided in small and big spheres. \* *p* < 0.05, \*\*\* *p* < 0.001 (Kruskal-Wallis ANOVA and Dunn's post-hoc test). (**B**) Live cell videomicroscopy analysis of one representative RES-CRC-SCs (#19RES) grown as 3D tumorspheres, left untreated or exposed to CHK1i, either alone or in combination with MRE11i or RAD51i. Images were taken every 20 min for up to 67 h (see Materials and Methods). Representative frames of the fate of one sphere per condition are shown, with numbers referring to the time passed from the beginning of the recording. Expulsion of one or more dead/inert cells or of one or more dead/inert cell aggregates are depicted with a red arrow point (**Exp\_not viable**), while sphere disaggregation and/or death with a dark arrow point (**Death**). The fate of all spheres analyzed (at least 50 spheres per condition) are represented on the right using the indicated color code. In such "sphere fate profile", each single sphere is depicted by a hyphen, with the first and last spheres analyzed also illustrated by a number. The following events are included: (i) expulsion of one or more viable cells or cell aggregates (**Exp\_viable**; in green), (ii) sphere fusion (**Fusion**; in orange), (iii) expulsion of one or more cells or cell aggregates with an apoptotic or inert morphology (**Exp\_not viable**; in red), (iv) sphere disaggregation and/or death (**Death**; in dark). The growth of each sphere was determined by measuring the area of the sphere at the beginning and at the end of the recording, and then calculating the ratio of the latter on the former. We considered as growing spheres only those with a growth ratio higher than 1.3. **Growing** and **not-growing** spheres are respectively colored in blue and grey. Numbers indicate the total number of spheres for each condition, counted in two separate videos. In the histogram in the panel, results are expressed as percentages of abnormal spheres (comprising spheres not growing and/or disaggregated/dead spheres) on the top and of spheres undergoing multiple (i.e., more than three) rounds of expulsion of inert/non-viable cells or cell aggregates on the bottom. For more information about the experiments, categories, and exclusion criteria see Materials and Methods. See also Supplementary Videos. Dose range in A–D: 100 nM CHK1i, 20 µM MRE11i for #19RES or 30 µM MRE11i for #1RES, 7.5 µM RAD51i; a.u., arbitrary units. Associated supplementary figure: Supplementary Figure S5.

#### **3. Discussion**

The ATR-CHK1 axis is particularly relevant for the survival of CSCs, which often display high levels of replication stress, and, accordingly, ATR and CHK1 inhibitors are being explored for the design of anti-CSC therapies [20,22,23]. Moreover, the antineoplastic activity of ATR and CHK1 inhibitors, including prexasertib, is currently investigated in ongoing clinical trials (https://www.clinicaltrials.gov/). However, a significant fraction of CSCs is intrinsically resistant to these therapies, and resistance mechanisms also emerge. Here, we provided evidence that the targeting of either MRE11 or RAD51 sensitizes primary CRC-SCs to the CHK1 inhibitor prexasertib by inducing a mitotic catastrophe process culminating in caspase-dependent cell death.

Results from this study indicate that sensitization to prexasertib by RAD51 or MRE11 inhibitors involves the boost of replication stress, which reinstates CRC-SC dependence on the function of the ATR-CHK1 pathway of the replication stress response. This is in line with previous evidence showing enhanced anti-CSC activity of ATR-CHK1 inhibitors due to the induction of replication stress by agents including (but not limited to) gemcitabine, irinotecan and inhibitors of PARP1, RRM2 or WEE1 [20,25,26,32–34]. However, here, we showed that the two identified prexasertib-based regimens exerted a rather broad impact on the DDR of CRC-SCs, simultaneously impairing several cellular processes for the preservation of genomic stability. Indeed, concomitant inhibition of CHK1 and RAD51 or MRE11 deregulated not only the DNA replication process but also cell cycle progression and ultimately cell division, resulting in a general reorientation of the DDR toward cytotoxicity. This evidence is relevant for cancer therapy, as it suggests that prexasertibbased combinations can potentially overcome the reported heterogeneity in the therapeutic response of CSCs, which constitutes one major challenge in cancer therapy. In particular, CSC sensitivity to ATR-CHK1 inhibitors could be limited by a variety of cytoprotective mechanisms characterizing CSCs, encompassing (among others) strong DNA damage signaling and repair, cell cycle checkpoint proficiency, elevated tolerance to DNA damage, and limited apoptosis induction (reviewed in [10]). As a consequence, therapeutic regimens that simultaneously target multiple of these aspects—like those based on the combination of CHK1+MRE11 or CHK1+RAD51 inhibitors—can be particularly effective in eradicating CSCs, also limiting the potential development of acquired resistance.

At the mechanistic level, in this study, we demonstrated that combined inhibition of CHK1 and either MRE11 or RAD51 leads to uncontrolled cell cycle progression and untimely mitotic entry of cells with ongoing replication stress. Usually, the proliferation of replication stressed cells is limited by the activation of the intra-S and G2/M checkpoint, both of which depend on the ATR-CHK1 axis [35,36] and, so, are silenced on its abrogation. Consistently, under prexasertib-based regimens, we found illicit proliferation and mitotic entry of CRC-SCs with unreplicated and damaged DNA, as shown by the presence of premature mitoses (pH3<sup>+</sup> cells with DNA content between 2*n* and 4*n*) and of mitotic damage (mitotic cells displaying γH2AX foci). Hence, we propose a model in which premitotic defects linked to replication stress emerging upon CHK1+MRE11 or CHK1+RAD51 inhibition are eventually transmitted into mitosis, threatening genomic stability, affecting sister chromatid segregation, and resulting in the activation of mitotic catastrophe.

At this regard, the inhibition of RAD51 or MRE11 may constitute the initiating factor of prexasertib sensitization. Indeed, RAD51 is a homologous recombination (HR) player also contributing to the stabilization, regression and restart of stressed DNA replication forks [37–41], meaning that its inhibition can impair the replication stress response. Similar considerations apply to MRE11, which has a pleiotropic impact on the DDR. In particular, a controlled and limited resection by MRE11 is crucial for the efficient restart or repriming of stalled forks [42,43]. In this context, uncontrolled fork degradation by MRE11, which occurs in conditions of fork instability or unprotection [44,45], is restricted by factors including RAD51 [46]. Moreover, MRE11 as a component of the MRE11-RAD50-NBS1 (MRN) complex has roles in the G2/M checkpoint, regulating the interphase-prophase transition, and also acts in sister chromatid segregation during mitosis [47–49]. Further studies will uncover the precise mechanisms through which RAD51 or MRE11 inhibitors affect mitosis entry and execution upon concurrent CHK1 inhibition. Irrespective of these mechanistic unknowns, we demonstrated that mitotic catastrophe by RAD51+CHK1 or MRE11+CHK1 inhibitors resulted in tumorsphere disaggregation and CRC-SC death via caspase-dependent apoptosis. The particular proneness of CRC-SCs to mitotic catastrophe suggested by this and our previous study [26] can be potentially exploited for the design of effective colorectal cancer therapy.

#### **4. Materials and Methods**

#### *4.1. Cell Lines, Culture Conditions and Chemicals*

Media, supplements and plasticware for cell culture were supplied by Thermo Fisher Scientific (Waltham, MA, USA), Sigma-Aldrich (Millipore-Sigma, Merck Group, St. Louis, MO, USA) and Corning Life Sciences (Corning, NY, USA). Colorectal cancer cells (CRC-SCs) used in this study were previously isolated and established from patient sample following the standards of the institutional ethics committee on human experimentation (authorization no. CE5ISS09/282) as reported in [50]. Informed consent was requested in this previous study. The methods for CSC authentication, validation and cultivation are reported in [20,50], while generation of CRC-SCs resistant to CHK1 inhibitors (RES-CRC-SCs) is described in [26]. Adavosertib (#S1525), B02 (#S8434), KU-60019 (#S1570), mirin (#S8096), NU7026 (#S2893), prexasertib (#S7178), Q-VD-Oph (#S7311), triapine (#S7470), and VE-821 (#S8007) were supplied by Selleck Chemicals (Houston, TX, USA), while dimethyl sulfoxide (DMSO, #5879) by Sigma-Aldrich. Twenty-four hours before each

experimentation, CRC-SCs were dissociated at single cells, then counted and seeded in specific multi-well plates. For live microscopy and videomicroscopy studies, cell seeding was carried out 48 h before the treatment to allow tumorsphere formation.

#### *4.2. Cell Proliferation and Viability*

To assess proliferation and survival, CRC-SCs were dissociated at single cells and seeded at a density of 6–8 <sup>×</sup> <sup>10</sup><sup>3</sup> cells per well in 96-well plates, in a volume of 100 µL of medium per well. After 24 h, CRC-SCs were treated according to specific experimentations and cell viability/proliferation was determined by assessing ATP levels via CellTiter-Glo® Luminescent Cell Viability Assay (#G7572, Promega, Madison, WI, USA) with a multimode microplate reader (DTX-880; Beckman Coulter, Brea, CA, USA). Experiments were carried out in triplicate and repeated at least three independent times, with results expressed as means of triplicate values or of duplicate values when we encountered technical problems with one replicate, as reported in the statistical procedures. The heatmap in Figure 1A illustrates the percentage of viable RES-CRC-SCs determined via CellTiter-Glo® Assay upon normalization of treated on untreated conditions using data reported in Supplementary Table S1. Drug sensitivity heatmap was generated using a Python script (Python Software Foundation, Wilmington, DE, USA; https://www.python.org/) and the Seaborn library (10.5281/zenodo.592845). The heatmap scale goes from 100 (blue; i.e., RES-CRC-SCs are all alive) to 25 (dark red) in consideration of the fact that 25% is the minimum of viability obtained for RES-CRC-SCs using the combination prexasertib+triapine.

#### *4.3. Cytofluorometric Studies*

Flow cytometry studies aimed at determining cell cycle profile, mitosis fraction, DNA damage and/or apoptosis of CRC-SCs were performed as previously reported [26]. The following primary antibodies were used: cleaved caspase 3 (CASP3; #9661) and phosphohistone H3 (pH3; #3377) from Cell Signaling Technology (Danvers, MA, USA), and γH2AX (#05-636) from Merck Millipore. Secondary antibodies used were: Alexa Fluor® 488-goat anti-mouse and 647-donkey anti-rabbit ((#A-21121 and #A-31573). These antibodies were provided by Thermo Scientific. Antibody dilutions and the Research Resource Identifiers (RRIDs) are in [26]. The DNA intercalant DAPI (#D1306, from Thermo Scientific) at 10 µM was employed to stain the DNA. The samples were acquired by a BD FACSCelestaTM flow cytometer (BD Biosciences, BD, Franklin Lakes, NJ). Data and statistical analyses were performed with the FlowJo software (FlowJo LLC, BD), gating on events showing normal forward and side scatter (FSC and SSC) parameters, on singlets and (for cell cycle analysis) upon exclusion of the sub-G<sup>1</sup> fraction.

#### *4.4. Immunofluorescence Studies*

Immunofluorescence studies aimed at detecting DNA damage markers in CRC-SCs were performed as reported in [26]. Briefly, upon permeabilization, the cells were blocked for 30 min in 5% (*w*/*v*) BSA, 5% FBS and 5% normal goat serum (NGS) in PBS. Thereafter, samples were incubated with primary antibodies directed against cleaved CASP3 (1:400; #9661) or γH2AX (S139) (1:250; #05-636) at 4 ◦C. After overnight incubation, slides were stained with secondary Alexa Fluor conjugates (1:500) together with 10 µM Hoechst 33342 (#H1399). Fluorescence images were visualized, analyzed and captured with a Leica DMI3000 B microscope, using a 40× objective (HCX PL Fluotar, AN 0.60) or a 100× objective (HCX PL Fluotar, AN 1.3), the Leica DFC 310FX camera, and the LAS X software (all from Leica Microsystems, Wetzlar, Germany). Analyses were carried out directly at the microscope or using the ImageJ v1.8 software (National Institute of Health, Bethesda, MD, USA; https://imagej.nih.gov/ij/). To determine γH2AX positivity, at least 61 cells per condition and per experiment were analyzed for a total of (at least) 743 cells (in five experiments). We considered three categories of positivity: (1) "focal" staining, when the cell presents > five γH2AX foci, (2) "partially-diffuse" staining, when the cell presents a diffuse γH2AX positivity covering between less than half of the nuclei, and (3) "pan-nuclear"

staining, when the cell presents a diffuse γH2AX positivity covering more than half of the nuclei (see Figure 2). In Figure 3, cells were recognized as mitoses on morphological basis upon nuclear staining with Hoechst 33342. For CASP3 activation analysis, at least 261 cells per condition and per experiment were analyzed for a total of (at least) 923 cells (for three experiments). In such automated analysis, cells were considered positive for CASP3 when signal intensity calculated with ImageJ was above a predefined threshold size to account for background noise.

#### *4.5. Immunoblotting*

To detect protein levels, CRC-SCs were recollected, lysed, and subjected to westernblot as reported in [20]. Membranes were incubated with primary antibodies directed against MRE11 (1:1000; #sc-22767; RRID:AB\_2145247 and #sc-135992; RRID:AB\_2145244) from Santa Cruz Biotechnology (Dallas, TX, USA), PARP1 (#9542) from Cell Signaling Technology, RAD51 (1:1000; #sc-398587; RRID: AB\_2756353 and #sc-377467) from Santa Cruz Biotechnology, RPA32 (#A303-874A) from Bethyl Laboratories (Montgomery, TX, USA) and pRPA32 (S4/S8) (#A300-245A) from Bethyl Laboratories, as well as with antibodies recognizing β-actin (1:2000; #A5441; RRID:AB\_476744), cofilin (#3318, Cell Signaling Technology), nucleolin (1:1000; #14574; Cell Signaling Technology; RRID:AB\_2798519), or β-tubulin (#T4026). After overnight incubation with primary antibodies and washes, membranes were incubated for 1 h at RT with the appropriate horseradish secondary antibodies conjugated to peroxidase: the donkey anti-rabbit IgG (#GENA934 or RRID:AB\_772206), mouse anti-goat IgG (#sc-2354, Santa Cruz; RRID:AB\_628490), or sheep anti-mouse IgG (#GENA931). When not specified, antibody dilutions and RRIDs can be found in [26]. Chemiluminescence imaging was carried out with the Fujifilm LAS-4000 luminescent image analyzer (GE Healthcare, Chicago, IL, USA), or the G:Box Chemi-XX9 using the GeneSys Software v1.5.6 (Synoptics, Cambridge, UK). At least two independent experiments were carried out. Representative Western-blots are shown in the figure together with the densitometry plus quantification, which were performed with ImageJ v1.8 software. Full blots are presented in Supplementary Figures S1–S4.

#### *4.6. SYTOX Incorporation*

To determine the induction of apoptosis, CRC-SCs dissociated at single cells were resuspended in 90% CSC growth medium + 10% Matrigel Basement Membrane Matrix (Matrigel, #354230, Corning), at a density of 3.5 <sup>×</sup> <sup>10</sup><sup>5</sup> cells in 500 µL. CRC-SCs were then seeded in 24-well plates, incubated for 24 h in standard culture condition and finally treated for 48 h with prexasertib, mirin and/or B02, alone or in combination, as reported in figure legends. After the (co)treatment, cells were incubated with the vital dye SYOXTM (at 5 µM; #S7020, Thermo Scientific) together with the DNA dye Hoechst 33342 (at 8 µM) for 10 min. Cells were immediately analyzed using the Leica DMI3000 B fluorescence microscope equipped with a 20× objective (HCX PL Fluotar AN 0.40) (see above for further details). A number of at least eight randomly selected images were analyzed with ImageJ v1.8 software (Fiji version) as follow. Briefly, nuclear fluorescence was used to discriminate single cells or small cell aggregate from spheres (threshold area: 1000 pixels), on which the green intensity (corresponding to dead cells) means were quantified and finally divided on Hoechst intensity. A minimum of 190 spheres per condition were analyzed.

#### *4.7. Videomicroscopy*

To determine the impact of prexasertib-based combinations on 3D tumorspheres, CRC-SCs were dissociated at single cells and then resuspended in 80% CSC medium + 20% Matrigel, at a density of 1 <sup>×</sup> <sup>10</sup><sup>5</sup> cells in a drop of 50 µL, and seeded in 24-well plates in a final volume of 500 µL. Upon 48 h incubation in standard culture conditions, cells were subjected to live videomicroscopy analysis using a Nikon LIPSI system (Nikon, Minato-ku, Tokyo, Japan) equipped with IRIS 15 photometrics camera allowing for standard culture cultivation. Images were taken every 20 min for up to 67 h, with a 20× long-range

objective (S-PLAN AN 0.4) and analyzed with ImageJ v1.8 software (Fiji version). The following criteria of exclusion were adopted. Spheres were excluded from analysis when: (1) the distance from another sphere was less than 13 µm, so to avoid problems due to excessive rounds of sphere fusion; (2) underwent more than one round of fusion with other spheres, thus becoming too big to be analyzed; (3) had a length for one of the two orthogonal diameters (which have been arbitrarily chosen in the first frame of the movie) less than 13 µm; (4) had a length for the first orthogonal diameter <30 µm and the second orthogonal diameter <40 µm, (5) the fate was not clear to define; and (6) their demise occurred within the first 12 h of the video recording (and thus presumably not caused by the treatment). The following events were retained as important: (1) expulsion of apparently viable cell(s) or cell aggregate(s), as determined by morphological criteria and the capability to divide and/or grow, (2) expulsion of apparently non-viable or inert cell(s) or cell aggregate(s), as determined by morphological criteria, (3) fusion with another viable sphere, and (4) disaggregation and death of the spheres, as determined by morphological criteria. In the histograms of Figure 5B, results are expressed as percentages of abnormal spheres (comprising bot disaggregated spheres and not-growing spheres) and of spheres undergoing multiple rounds of expulsion of non-viable cells/cell aggregates. In this figure, the growth of each sphere was determined by measuring the sphere area at the beginning and at the end of the recording, and then dividing the latter on the former value. Spheres were considered as expanding when the ratio was higher than 1.3.

#### *4.8. Statistical Procedures*

All the experiments were repeated in at least three independent instances, but in case of absent sub-significant trends, the sample size was increased to more than three (see figure legends or dedicated material method sections for the exact sample size, the presence of replicates, and data/replicate exclusion criteria). We evaluated the variance equality using the F-test or the Brown-Forsythe test, when appropriate. We calculated the Shapiro-Wilk normality test for all the continuous variables. For the variables that are normally distributed and according to the number of groups compared, we used unpaired Student T test or Welch's unpaired T-test, and one-way ANOVA followed by Bonferroni post-hoc test or Brown-Forsythe and Welch one-way ANOVA followed by Dunnett's T3 post-hoc test, when appropriate. For the variables that are not normally distributed or for less than three independent experiments, we applied Mann-Whitney test or Kruskal-Wallis test followed by Dunn's post-hoc test, according to the number of groups. Data (including normalization) were calculated and visualized as reported in [26]. The following softwares were used: Microsoft Excel (Microsoft, Redmond, WA, USA), Prism (v8.3.0, GraphPad Software, San Diego, CA, USA) and SPSS (SPSS v.21, SPSS Inc-IBM, Chicago, IL, USA).

#### **5. Conclusions**

In this study we identified RAD51 and MRE11 as two targets whose inhibition increases the sensitivity of CRC-SCs to CHK1 inhibitors. We also characterized the mechanism of CSC killing by CHK1+RAD51 and CHK1+MRE11 inhibitors, showing that it involves the induction of replication stress followed by progression of replication stressed CRC-SCs through the interphase and their premature entry into mitosis, ultimately leading to caspase-dependent mitotic catastrophe. These results support the future clinical development of prexasertib-mediated regimens in colorectal cancer patients.

**Supplementary Materials:** The following are available online at https://www.mdpi.com/article/10 .3390/cancers13081957/s1. Supplementary Figure S1. Analysis of replication stress in RES-CRC-SCs exposed to prexasertib-based regimens. Related to Figure 2. Supplementary Figure S2. Analysis of MRE11 or RAD51 levels in SENS-CRC-SCs and RES-CRC-SCs. Related to Figure 4. Supplementary Figure S3. Analysis of MRE11 or RAD51 levels in SENS-CRC-SCs and RES-CRC-SCs. Related to Figure 4. Supplementary Figure S4. Analysis of PARP1 cleavage in RES-CRC-SCs. Related to Figure 4. Supplementary Figure S5. CHK1 cooperates with MRE11 or RAD51 for RES-CRC-SC survival. Related to Figure 5. Supplementary Movies 1–12. Impact of MRE11+CHK1

inhibition and RAD51+CHK1 inhibition on CRC-SC organization, proliferation and survival. Related to Figure 5. Supplementary Table S1. Raw data of the heatmap presented in Figure 1A. Supplementary Information.

**Author Contributions:** L.M., S.S.A.R., and M.M. performed and analyzed experiments; A.S., S.V., C.G. and F.C. performed experiments; A.G. helped analyzing survival data; F.S. supervised and performed statistical analysis; G.M. supervised the project, designed and performed experiments, analyzed and interpreted data, and contributed to article writing; R.D.M. obtained funding, supervised the project, and contributed to article writing; I.V. obtained funding, supervised the project, designed experiments, analyzed and interpreted data and wrote the manuscript. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was supported by the Associazione Italiana per la Ricerca sul Cancro (AIRC, IG 2017 grant number 20417 to IV; AIRC 5 × 1000 grant number 9979 to RDM; Start-Up 2016 #18418 to AS), Ministero Italiano della Salute (RF\_RF-2018-12367044 to RDM; RF\_GR-2013-02357273 to AS). IV is also supported by a startup grant from the Italian Institute for Genomic Medicine (Candiolo, Turin, Italy) and Compagnia di San Paolo (Turin, Italy).

**Institutional Review Board Statement:** All procedures were in accordance with the ethical standards approved by Institutional Ethics Committee on human experimentation and with the Helsinki Declaration.

**Data Availability Statement:** The materials and data are available from the corresponding authors.

**Acknowledgments:** We thank Paola Di Matteo, Roberto Ricci, and Stefano Guida (Istituto Superiore di Sanità, Rome, Italy) for technical assistance. We also thank Denis Baev of the cytometry facility of the Italian Institute for Genomic Medicine (IIGM, Candiolo, Italy) for assistance in cytofluorimetric acquisitions.

**Conflicts of Interest:** The authors have no conflicts of interest to disclose.

#### **References**

