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Abstract

BACKGROUND:

Neuronatin (NNAT) determined by immunohistochemistry is a negative prognostic biomarker for breast cancer, independent of the major clinicopathological markers.

OBJECTIVE:

Here, we investigated whether NNAT is also a predictive biomarker for pathological remission after neoadjuvant chemotherapy.

METHODS

: One hundred and four breast cancer patients, treated with systemic neoadjuvant chemotherapy were included in this retrospective study. NNAT was detected in formaldehyde fixed, paraffin embedded primary cancer tissue by immunohistochemistry and an immuno-reactive score (IRS) determined. Pathological remission was scored according to Sinn and by evaluation of cytopathic effects. NNAT-IRS was correlated with clinicopathological parameters as well as relapse free and overall survival and for pathological remission after neoadjuvant therapy.

RESULTS:

NNAT IRS was an independent prognostic marker for relapse free and overall survival and the time from diagnosis to the “tumor-free” state. NNAT IRS was associated with Luminal-A tumors and correlated slightly negative with age and lymph-node metastasis. There was no significant correlation of NNAT-IRS with Sinn’s remission score, but with cytopathic effects of chemotherapy.

CONCLUSIONS:

We confirmed the prognostic impact of NNAT-IRS in an independent cohort of neoadjuvantly treated patients. Additionally, a correlation with a score for pathological remission under systemic neoadjuvant chemotherapy for breast cancer was found.

Keywords

Breast cancer neoadjuvant chemotherapy neuronatin remission score

1. Introduction

Breast cancer is the most frequent neoplasm in women worldwide and accounted for 2,088,849 new cases and 626,679 deaths worldwide in 2018 [1]. It is a heterogeneous disease and therapy is mostly planned according to TNM stage and immuno-histochemical analysis, determining hormone receptor status and mitosis count (Ki-67 proliferation index) [2]. Based on gene expression data, breast cancer is classified in the luminal-A and -B subtype, HER2 overexpressing and basal subtypes [3]. The immuno-histochemical determination of hormone receptor status and Ki-67 proliferation index is widely used as an estimate for these molecular subtypes in a clinical setting [4]. Whereas the estrogen receptor positive subtypes are treated with targeted therapies blocking the estrogen receptor or inhibiting aromatase [5, 6, 7], HER2/Neu overexpressing cases receive an antibody therapy against this epidermal growth factor receptor [8]. For the basal, triple negative subtypes, no established targeted therapy is in routine clinical use, but chemotherapy is applied regularly [9]. In the last decade a shift from the traditional therapy scheme, starting with surgery and then continuing with an appropriate adjuvant chemotherapy frequently combined with radiotherapy, towards systemic neoadjuvant chemotherapy has occurred [10]. Several studies showed that this is in several aspects superior over adjuvant treatment, however an increased rate of local reoccurrence might occur [11]. Nevertheless, neoadjuvant chemotherapy has become especially important for locally advanced breast cancer [12, 13].

Neoadjuvant therapy is considered effective, when pathologic complete remission (PCR) of the tumor occurs. A complete remission is accomplished when no remaining cancer cells are found after therapy in the surgically removed tumor residuals. As complete remission is frequently not achieved, remission is scored i.e. according to Sinn [14]. In this scoring system, tumor residuals are classified in four groups. Sinn score 0 indicates no effect of therapy, Sinn 1 shows significant tumor sclerosis, inflammation and cytopathic effects, Sinn 2 is characterized by extensive tumor sclerosis with only a few remaining tumor cells resulting in a residual tumor of less than 0.5 cm. In Sinn 3, no invasive tumor is seen and in Sinn 4, no tumor at all is found. Nevertheless, alternative scoring systems have also been developed [15].

We have recently showed that neuronatin (NNAT) determined by immuno-histochemistry is an independent unfavorable prognostic marker for breast cancer [16]. Neuronatin is a proteolipid occurring in two forms encoded by different splice products [17]. Such proteolipids function in the regulation of ion channels and NNAT is proposed to be particularly involved in Ca ${}^{2+}$ homeostasis [18]. NNAT can also form cytotoxic aggregates in Lafora disease [19] and is linked to inflammation and obesity via influencing several kinases such as p38-MAPK or ERK but not NF-kB [20]. For several cancers, such as glioblastoma multiforme, medulloblastoma, myxoid liposarcoma and lung cancer [21, 22, 23, 24, 25] NNAT overexpression has been observed and often attributed to a loss of methylation of the maternal allele [21, 26]. Besides this regulation by promoter methylation, the micro-RNA708 has been shown to downregulate NNAT in breast cancer, resulting in reduced intracellular Ca ${}^{2+}$ associated with reduced migration and metastasis [27]. This was also associated with the anti-tumor effect of metformin as this drug induces miRNA708-5p, which in turn downregulates NNAT. The resulting changes in Ca ${}^{2+}$ homeostasis in the endoplasmic reticulum (ER) caused ER stress and apoptosis in prostate cancer [28]. Concerning breast cancer, NNAT was reported to be related to incidence and outcome of ER-positive cases [29] and differentially expressed in a tamoxifen resistant cell line [30].

We here report on a follow up study of our retrospective evaluation of the prognostic value of NNAT for breast cancer [16]. We additionally investigated whether NNAT, determined by immuno-histochemistry, is also associated with pathological remission in neoadjuvantly treated breast cancer patients.

Table 1
Systemic treatment of patients stratified for hormone receptor expression. Significance was tested between treatments for receptor positive and negative cases using Fisher’s exact test. A significance value $p<$ 0.05 is indicated in italic. Monoclonal antibody (Ab), Tyrosine kinase inhibitor (TKI)

Patients	All	ER+	ER-	HER2+	HER2-
	103	59	44	49	55
Treatment
Paclitaxel or docetaxel (T)	93 (90.3%)	53 (89.8%)	40 (90.9%)	44 (89.8%)	49 (89.1%)
Epi- or doxyrubicin (A)	79 (76.7%)	45 (76.3%)	34 (77.3%)	33 (67.3%)	46 (83.6%)
Cyclophosphamid (C)	77 (74.8%)	43 (72.9%)	34 (77.3%)	32 (65.3%)	45 (81.8%)
T $+$ A	77 (74.8%)	44 (74.6%)	33 (75.0%)	33 (67.3%)	44 (80.0%)
A $+$ C	76 (73.8%)	43 (72.9%)	33 (75.0%)	31 (63.3%)	45 (81.8%)
T $+$ C	74 (71.8%)	42 (71.2%)	32 (72.7%)	30 (61.2%)	44 (80.0%)
T $+$ A $+$ C	74 (71.8%)	42 (71.2%)	32 (72.7%)	31 (63.3%)	43 (78.2%)
HER2 directed Ab				38 (77.6%)
HER2 directed TKI				4 (8.2%)
Aromatase inhibitor		19 (32.2%)
Tamoxifen		38 (64.4%)

2. Materials and methods

2.1 Experimental subjects

Female patients (104) with primary breast cancer undergoing neoadjuvant systemic chemotherapy from the department of gynecology of the Otto von Guericke University (Magdeburg) were included into this retrospective study from 2010 to 2016. A positive ethical vote was obtained from the ethics committee of the Otto von Guericke University of Magdeburg at the medical faculty (AKZ 114/13) according to the statement of the central German ethics commission on the reuse of patient material for research [31]. Clinical and pathological data were collected from the patients’ files in the pathology and gynecology department of the Otto von Guericke University. A mean follow up of 217 weeks (min $=$ 20 weeks, max $=$ 484 weeks) could be achieved. Patients were mainly treated using a combination of a taxane, anthracyclin and cyclophosphamid (TAC scheme). In some cases this scheme was altered according to the patient’s condition. According to receptor status, this treatment was combined with a targeted therapy directed against HER2 or estrogen. HER2-positive cases mainly received an anti-HER2-antibody based therapy, whereas for ER-positive cases, Tamoxifen was the most frequently used drug compared to the aromatase inhibitor Letrozol (Table 1).

2.2 Antibodies and Immuno-histochemistry

In contrast to our earlier study, an automated method for NNAT staining was applied. For evaluation of this procedure, some slides of the former patient cohort were stained and the results compared with the results of the manual procedure. After establishing the improved staining method, specimen from the new cohort were stained as follows: In short, 2 $\mu$ m slices of paraffin embedded tissue were prepared and deparaffinized using xylol. All further steps were performed in a Bechmark Ultra staining system (Ventana). Epitope-retrieval was done using CC1 buffer for 92 min at 100 ${}^{\circ}$ C. NNAT antibody (ab27266, abcam) was applied in a 1/50 dilution in Ventana blocking buffer. Detection was performed using the OptiView DAB IHC Detection Kit (Ventana) without signal amplification. Counter staining of the nuclei was done using hematoxylin. Slides were embedded and evaluated by a pathologist for staining intensity (0 $=$ no staining, 1 $=$ weak staining, 2 $=$ intermediate staining, 3 $=$ strong staining) of tumor cells and percentage of positive tumor cells in 10% increments. An immunoreactive score (IRS) was calculated by multiplying both values and further division by 10. From the abladate, sections were stained with hematoxylin-eosin and scored for pathological remission. First, the regression score according to Sinn was determined, second cytopathic effects were scored according to a score from 0 to 3. In score 0, there were no substantial changes in the cancer cell morphology comparing with the initial biopsy. In score 1, there was substantial change in the cell morphology with increase in the size of the nucleus and/or cytoplasm as well as changes in the chromatin morphology. In score 2, the changes in the cancer cell size and morphology were very pronounced. In score 3, no remaining evaluable cancer cells could be found.

2.3 Analysis of gene expression databases

We accessed the METABRIC gene expression data for breast cancer via cBioportal [32] and plotted the co-expression data on this website. The 20 most significant co-expressed genes were selected and used for an overrepresentation analysis on the enrich-database website [33].

2.4 Statistical analysis

All statistical calculation were done with SPSS vers. 26 (IBM). For evaluation of a correlation of categorical parameters, cross tabulation with Fisher’s exact p was applied. For correlation of ordinal parameters, Spearman correlation factor was calculated. Survival probability distribution was analysed using the Kaplan Meier method. Equality of the survival curves was tested using the log rank and the Breslow method. Univariate and multivariate cox proportional hazard regression was used to determine hazard ratios and significance of prognostic factors. Generally, a $p$ -value of less than 0.05 was interpreted as significant; a value less than 0.1 as statistical trend.

Figure 1.

Microphotographs demonstrating NNAT-immuno-histochemistry results. All tumors depicted here, are classified as “no special type” (NST). A–C and G–I show examples for NNAT staining intensity at low magnification (A, B weak intensity 1; C, medium intensity 2; G, H, I strong intensity 3). Higher magnifications corresponding to the specimens depicted in A–C and G–I are shown in D–F and J–L. Scale bars represent 250 $\mu$ m and 50 $\mu$ m, respectively.

3. Results

3.1 Correlation with clinicopathological parameters

NNAT staining intensity and percent positive tumor cells were determined and an immuno-reactive score (IRS) calculated (Fig. 1). Then we investigated a possible association of this NNAT-IRS with standard parameters (Table 2) such as age, hormone receptor – and lymph node status. This was done in cross-tabulation tests and by determining the Spearman correlation factor. For cross tabulation, the cut off for NNAT low versus high was set to 17, which was the optimal cutoff in survival analysis (see below). In this cross tabulation tests, NNAT turned out to be correlated only to the Luminal-B subtype. Additionally, in Spearman correlation analysis, a weak negative correlation with age and lymph node status and a positive correlation with Ki-67 proliferation index was seen (Table 3).

Table 2
Study cohort with respect to the distribution of clinico-pathological parameters and NNAT expression. NNAT was considered as high when IRS was higher than 17. Fisher’s exact p or Spearman correlation $p$ is given

Parameter	Number	%	NNAT low/high (%high)
Total	104	100.0	58/46 (55.8)
Average age at diagnosis	54.5
	$+/-$ 12.2
Menopausal state (age $>$ 55)			$P=$ 0.16
Pre-menopausal	59	56.7	29/29 (49.2)
Post-menopausal	45	43.3	30/16 (35.6)
Estrogen receptor			$P=$ 0.84
ER-positive	60	57.7	34/26 (43.3)
ER-negative	44	42.3	24/20 (45.5)
Progesterone receptor			$P=$ 0.85
PR-positive	51	49.0	29/22 (43.1)
PR-negative	53	51.0	29/24 (45.3)
HER2/Neu			$P=$ 0.84
HER2-negative	56	53.85	32/24 (42.9)
HER2-positive	48	46.15	26/22 (45.8)
Luminal-A/-B			$P=$ 0.047
Luminal-A	17	16.35	4/17 (23.5)
Luminal-B	43	41.35	22/43 (51.2)
Triple negative			$P=$ 0.64
Yes	23	22.12	14/9 (39.1)
No	81	77.88	44/37 (45.7)
Regression grade sinn (tumor) [14]			$P=$ 0.36 (interval by interval)
No effect (0)	0	0.00	0/0 (0.00)
Resorption and tumor sclerosis (1)	63	60.58	36/27 (42.9)
Minimal residual invasive tumor [ $<$ 0.5 cm] (2)	14	13.46	10/4 (28.6)
Residual noninvasive tumor only (3)	8	7.69	3/5 (62.5)
No tumor detectable (4)	19	18.27	9/10 (52.6)
Sinn $>$ 1			$P=$ 0.84
Yes	41	39.39	22/19 (46.3)
No	63	60.58	36/27 (42.9)
Cytopathic effect			$P=$ 0.146 (ordinal by ordinal)
0	10	9.71	6/4 (40.0)
1	54	52.43	33/21 (40.4)
2	13	12.62	8/6 (48.0)
3	27	25.24	12/15 (57.7)
Ki-67			$P=$ 0.21
Low $<$ 5%	3	2.88	3/0 (0.00)
Intermediate 5–24%	31	29.81	18/13 (41.9)
High 25–39%	25	24.04	14/11 (44.0)
Very high $>$ 40%	44	42.31	22/22 (50.0)
Not assessable	1	0.96
T-state			$P=$ 0.47
T0	25	24.04	12/13 (52.0)
T1	29	27.88	18/11 (37.9)
T2	31	29.81	16/15 (48.4)
T3	10	9.62	6/4 (40.0)
T4	9	8.65	6/3 (33.3)
Lymph node metastasis			$P=$ 0.39
N0	65	63.1	34/31 (47.7)
N1	28	27.2	19/9 (32.1)
N2	9	8.7	4/5 (55.6)
N3	1	1.0	1/0 (0.0)
N0			$P=$ 0.31
Yes	65	63.1	34/31 (47.7)
No (N1 to N3)	38	36.9	24/14 (36.8)

Table 3

Spearman correlation analysis of NNAT IRS with clinicopathological parameters. ER, PR and HER2 are represented as immunoreactive score. Ki-67 as % positive tumor cells. Positive lymphnodes were classified according to TNM stage as shown in Table 2

Parameter	Correlation factor	Significance
Sinn remission grade	0.12	0.213
Age	$-$ 0.23	0.017
Positive lymphnodes (N)	$-$ 0.2	0.04
ER	$-$ 0.06	0.551
PR	$-$ 0.08	0.399
Her2/Neu	0.04	0.723
Ki-67	0.193	0.052
Cytopathic effects	0.29	0.003

3.2 Survival analysis

Survival analysis was done using the Kaplan-Meier algorithm. We first investigated the time from diagnosis until the patients were declared as “tumor-free” and underwent surgery. Interestingly, the presence of high NNAT was associated with a longer median time from diagnosis until being tumor free (28.4 and 31.3 weeks Breslow estimator (generalized Wilcoxon) $p=$ 0.03) (Fig. 2A). We then analysed the major clinico-pathological subgroups separately (Fig. 3). We focussed on receptor expression, the luminal-A/-B subtypes and node positive ( $>$ 1) as well as Ki-67 status. For the time until the tumor-free state, the outcome for and luminal-B was significantly worse compared to luminal-A. However in all subgroups, tumors highly expressing NNAT were always less favorable than NNAT-low cancers. This was significant for HER2-negative cases ( $p=$ 0.02) and cases that express any of the receptors ( $p=$ 0.05). We also observed a statistical trend for Ki-67-high and ER-positive tumors. However, as case numbers were rather low in most subclasses, the significance level of $p<$ 0.05 (Breslow) was not reached.

Figure 2.

Kaplan-Meier survival curves, demonstrating the impact of NNAT-IRS on prognosis. Time from diagnosis to tumor-free state (2A) was analyzed and significance determined using the Breslow estimator. Relapse free survival was analyzed for the time from diagnosis to relapse (2B) and overall survival was defined as time from the tumor free state until death (2C).

Figure 3.

Kaplan Meier survival analysis of the time from diagnosis to the tumor free state. Survival curves are stratified for lymph node-, Ki-67-, ER-, PR-, HER2-status, luminal-A versus -B and triple negative breast cancers. Every tumor subtype is then further analyzed for the impact of NNAT expression. Number of cases and significance (Breslow estimator) is given.

In terms of relapse free survival (RFS), we analyzed both, the time from diagnosis and from the tumor-free state until occurrence of a local relapse. Here, the time from diagnosis to relapse depended significantly on NNAT-IRS (log rank $p=$ 0.05) but when starting from the tumor free state, only a statistical trend became obvious (long-rank $p=$ 0.076) (Fig. 2B).

When analyzing tumor subtypes separately, lymph node status was the only significant prognostic factor for RFS (Fig. 4). When the subtypes were further stratified for NNAT expression, NNAT became significant for lymph node positive (N1) and tumors that express any of ER, PR or HER2. However, there were clear trends ( $p<$ 0.1) for ER+, PR-, PR+ and HER2+. Again, the actual numbers of cases was low for the subtypes, which clearly reduces the statistical power of these data. Overall, NNAT high expressing tumours had an unfavourable prognosis as the Kaplan-Meier curves for NNAT-high were always below the curves for NNAT-low expressing cases. For overall survival (OS), a similar analysis was done, in this case, when the starting point was “first diagnosis” NNAT correlated weaker in comparison to starting from the “tumor-free” state (log-rank $p=$ 0.079 and 0.031 respectively) (Fig. 2C). For overall survival (OS) calculated from tumor-free to death, N1 was again the most significant prognostic factor and Ki-67-high showed a statistical trend for worse prognosis (Fig. 5). NNAT expression was a significant negative prognostic factor for N1, ER+ and PR+ and non-TNBC cases. A trend was observed for Ki-67-low, and luminal-A tumors. Similar to RFS, all NNAT-high OS curves are indicating unfavourable prognosis, but due to low case numbers most data were not significant.

Figure 4.

Kaplan Meier survival analysis of relapse free survival from time of diagnosis to relapse. Survival curves are stratified for lymph node-, Ki-67-, ER-, PR-, HER2-status, luminal-A versus -B and triple negative breast cancers. Every tumor subtype is then further analyzed for the impact of NNAT expression. Number of cases and significance (log-rank p) is given.

Figure 5.

KaplanMeier analysis of overall survival determioned from tumor free state until death. Survival curves are stratified for lymph node-, Ki-67-, ER-, PR-, HER2-status, luminal-A versus -B and triple negative breast cancers. Every tumor subtype is then further analyzed for the impact of NNAT expression. Number of cases and significance (log-rank p) is given.

Table 4

Cox regression analysis. RFS was calculated from date of diagnosis to event. OS was calculated from date being “tumor-free” to death. For multivariate cox regression analysis, NNAT HR was adjusted to ER, PR, HER2 and N status. Only significant factors represented in the final cox regression model are shown

Factor	Hazard ratio	95% CI	Significance	Hazard ratio	95% CI	Significance
	RFS			OS
Univariate cox-regression analysis
ER	0.74	0.34–1.61	0.45	0.45	0.15–1.36	0.16
PR	0.70	0.33–1.50	0.37	0.51	0.17–1.58	0.24
HER2	1.06	0.50–2.26	0.88	0.66	0.22–2.02	0.46
N	5.05	5.05–2.12	$<$ 0.001	3.56	1.10–11.57	0.035
NNAT	2.13	0.98–4.63	0.056	3.49	1.04–11.70	0.042
Multivariate cox-regression analysis
ER				0.19	0.05–0.66	0.009
N	9.33	3.45–25.19	$<$ 0.001	9.38	2.43–36.18	0.001
NNAT	2.99	1.30–6.85	0.01	6.45	1.75–23.80	0.005

Figure 6.

Kaplan-Meier survival analysis for relapse free survival depending on NNAT and PPAR $\gamma$ (PPARG) mRNA abundance. Analysis was performed at the KM-plotter-website [39].

In cox regression analysis (Table 4) NNAT turned out to be a significant prognostic factor for OS, but not RFS although a statistical trend could be observed. For RFS, only the presence of infiltrated lymph nodes (N $>$ 0) was significant. When the hazard ratio of NNAT was adjusted for ER, PR, HER2 and N (Table 4), NNAT turned out to be an independent prognostic factor for RFS as well as OS. Lymph node invasion was again the most significant factor and the estrogen receptor became also a significant prognostic factor for OS.

3.3 Pathological complete remission

We then correlated NNAT with scores for remission after neoadjuvant chemotherapy. First, we investigated the regression grade according to Sinn [14]. This rather crude score classified most tumors (61 %) in grade 1 characterized by tumor sclerosis, inflammation and substantial cytopathic effect. Here we saw no correlation in either cross tabulation nor in Spearman correlation (Tables 2 and 3) with NNAT-IRS. We then prepared a more refined scoring for the cytopathic effect, ranging from 0 (no effect) to 3 (severe effect). Applying this score, resulted in a significant correlation with NNAT-IRS (Table 3). Thus, NNAT-IRS was clearly related with this indicator for pathological remission.

3.4 Database analysis

In order to develop further ideas on the molecular function of NNAT, we identified co-expressed genes in the METABRIC database of breast cancer [34]. The 20 most significantly correlated genes (Table 5) were used for an overrepresentation study performed on the enrich database website [33]. Here, the most significant hits were associated with lipid metabolism, especially regulated by PPAR $\gamma$ (Table 6), which was also one of the top co-expressed genes.

Table 5
Co expression analysis for NNAT using the METABRIC [34] gene expression data

Correlated gene	Spearman’s correlation	$p$ -value
PLIN1	0.247	5.74E-28
COL14A1	0.240	1.72E-26
AQP7	0.239	2.41E-26
ACSM5	0.239	2.62E-26
PLIN4	0.237	6.32E-26
TEK	0.236	1.28E-25
PLAC9	0.231	1.12E-24
ABCA8	0.231	1.68E-24
CIDEC	0.228	6.34E-24
LIPE	0.228	6.58E-24
SVEP1	0.227	8.05E-24
MRAP	0.226	1.49E-23
ADH1A	0.225	1.88E-23
FHL1	0.225	2.37E-23
JAM2	0.224	4.18E-23
CCL14	0.223	5.41E-23
EBF3	0.223	6.58E-23
C6	0.223	6.84E-23
PPARG	0.223	6.88E-23
HSPB6	0.222	8.32E-23

Table 6

Overrepresentation analysis of NNAT co-expressed genes using the enrichr-database [33]. Only significant terms (adjusted $p$ -value) are shown

Term	Overlap	Adjusted $p$ -value
PPAR signaling pathway	4/74	2.46 E-4
Regulation of lipolysis in adipocytes	3/55	0.0033

4. Discussion

In this study, we were interested in the predictive potential of NNAT on pathological remission upon systemic neoadjuvant chemotherapy. Furthermore, we aimed at reproducing our earlier data on NNAT and relapse free and overall survival in a novel cohort of breast cancer patients. We also established an automated staining procedure for NNAT, which makes this biomarker more appropriate for routine diagnostics. In contrast to our earlier study, only patients treated by systemic neoadjuvant chemotherapy were included in this study and the treatment schemes have changed over the years. As a result, HER2-positive tumors were regularly targeted with specific antibodies such as trastuzumab. Additionally, due to the selection for neoadjuvant chemotherapy, the study cohort contained tumors that are more aggressive. Nevertheless, the impact of NNAT on survival data and the correlation to clinico-pathological features are similar to our earlier study. However, there are some minor differences to the results from the previous study, as i.e. we now observed a more pronounced effect in ER-positive cases than in negative cases for overall survival (Fig. 5).

The second result concerned the predictive potential of NNAT-IRS for the neoadjuvant treatment. Surprisingly, NNAT-IRS significantly correlated with the length of the interval from diagnosis to the “tumor-free state” (Fig. 2A). This interval reflects the speed of shrinking of the tumor and the possible occurrence of a progress under therapy. The dependency of this interval upon NNAT also influences the survival analysis. We therefore saw a difference in statistical significance, when starting the analysis from the date of diagnosis or the time of being “tumor-free”.

We then investigated the correlation of NNAT-IRS with the remission score according to Sinn or determined by the cytopathic effects caused by the therapy. The majority of cases was within the group of ”Sinn 1” defined by “increased tumor sclerosis and/or resorption with clear cytopathic effect” [14]. Using this scoring system, no statistical significance could be found. We therefore decided to replace it with a more detailed score based on the morphology of the cytopathic effects caused by the therapy. Indeed, for this score a highly significant correlation with NNAT-IRS was found. Unfortunately, we could not analyze the impact of NNAT on the type of therapy in more detail, as therapy regimes in this cohort were too divers. Nevertheless, we conclude, based on the dependence of the time until “tumor free” and the correlation with the cytopathic score that NNAT might be predictive for the efficacy of neoadjuvant chemotherapy. Nevertheless, this should be investigated in a larger cohort with more clearly defined therapy regimes.

As the molecular function of NNAT for cancer biology is still not completely understood, more efforts should also be taken for a detailed functional analysis of this protein and the associated pathways. We have pointed out that his proteolipid modulates cellular Ca ${}^{2+}$ homeostasis and suggested a relation with the G-protein coupled estrogen receptor GPER as this protein triggers Ca ${}^{2+}$ -fluxes upon stimulation [35]. As a starting point to develop further hypothesis on the function of NNAT in cancer, we here analyzed the METABRIC cohort of breast cancer patients for genes co-expressed with NNAT.

Unexpectedly, the NNAT co-expressed genes in breast cancer were significantly associated with “PPAR $\gamma$ regulated lipid metabolism” (Table 6). Indeed, the PPAR $\gamma$ -pathway is important for breast cancer biology [36, 37] and prognosis and PPAR $\gamma$ also influences Ca ${}^{2+}$ -homeostasis [38]. It is therefore reasonable that NNAT has also an impact on lipid metabolism in breast cancer cells. We then assessed gene expression data on the KM-plotter [39] website to investigate the prognostic value of NNAT and PPAR $\gamma$ (PPARG) further on mRNA level (Fig. 6). Kaplan Meier analysis for both genes was indeed highly significant. Interestingly, a high mRNA abundance was prognostic for favorable outcome for both genes. At first glance, this is in conflict with the protein data presented here, however an inverted regulation of mRNA and protein levels has been seen for other genes as well and can be explained by post-transcriptional regulation, such as a high turnover-rate of the protein despite of high mRNA abundance.

In conclusion, we here present evidence that NNAT could become a more important prognostic biomarker for breast cancer in the future. This was also evident for the prediction of the remission upon neoadjuvant chemotherapy, but only when a remission score based on the cytopathic effect was used. Nevertheless, the molecular function of NNAT should be investigated in more detail to identify potential therapeutic targets related to neuronatin.

Footnotes

Acknowledgments

The authors wish to thank the members of the histochemistry laboratory of the Institute of Pathology, Magdeburg for their great support and excellent work. This investigation has been funded by institutional funds only.

Conflict of interest

The authors declare that no conflict of interest exists.

Author contributions

Conception: NN, JH, TK, AI

Interpretation or analysis of data: WP, NN, PC

Preparation of the manuscript: WP, NN, PC

Revision for important intellectual content: JH, NN

Supervision: NN, PC

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The predictive potential of Neuronatin for neoadjuvant chemotherapy of breast cancer

Abstract

BACKGROUND:

OBJECTIVE:

METHODS

RESULTS:

CONCLUSIONS:

Keywords

1. Introduction

2.1 Experimental subjects

2.2 Antibodies and Immuno-histochemistry

2.3 Analysis of gene expression databases

2.4 Statistical analysis

3.1 Correlation with clinicopathological parameters

Table 2 Study cohort with respect to the distribution of clinico-pathological parameters and NNAT expression. NNAT was considered as high when IRS was higher than 17. Fisher’s exact p or Spearman correlation p is given

3.4 Database analysis

Table 5 Co expression analysis for NNAT using the METABRIC [34] gene expression data

Footnotes

Acknowledgments

Conflict of interest

Author contributions

References

Table 2
Study cohort with respect to the distribution of clinico-pathological parameters and NNAT expression. NNAT was considered as high when IRS was higher than 17. Fisher’s exact p or Spearman correlation $p$ is given

Table 5
Co expression analysis for NNAT using the METABRIC [34] gene expression data