Molecular characterization of the residual disease after neoadjuvant endocrine therapy in ER+/HER2- breast cancer uncovers biomarkers of tumour response

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López-Velazco, Sara Manzano, Kepa Elorriaga, Maria Otaño, and 14 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4310954/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Neoadjuvant endocrine therapy (NET) in oestrogen receptor-positive (ER+) /HER2-negative (HER2-) breast cancer (BC) allows an in vivo evaluation of treatment sensitivity by the monitoring of tumour response and offers the opportunity of personalized BC therapy. However, the lack of reproducible biomarkers to assess response and long-term prognosis after NET, beyond Ki67 levels and preoperative endocrine prognostic index score (mPEPI), is a significant barrier to increase its indications. Methods In this study we searched clinically relevant molecular reporters of NET response and prognosis in a multicentre population of ER+/HER2- BC patients by using: PAM50 gene expression panel, protein evaluation of key proteins involved in tumorigenesis and HER2-Low status evaluation. Results On a cohort of 131 patients our results show that PAM50 categorization (Luminal A vs Luminal B and ROR high vs ROR Low) predicts response to NET, with Luminal A and low ROR score tumours showing better response and prognosis than Luminal B and high ROR score. Moreover, tumours changing from Luminal A to Normal-like after NET presented a significant larger decrease in Ki67 levels at surgery, lower mPEPI score and a lower tumour cellularity size than those with persistent Luminal A status. In addition, we identify that the percentage of p53 positive cells in pre- and post-NET samples are associated with response or prognosis to NET. Conclusion Our findings highlight the change of intrinsic subtype to normal-like after NET as a putative biomarker characterizing a population that benefit highly from NET. If considered conjointly with the pathologic evaluation of response, we could characterize a tumour population benefiting most from endocrine approach and bearing a potential very good prognosis. Neoadjuvant endocrine therapy aromatase inhibitors PAM50 normal-like subtype HER2-low p53 preoperative endocrine prognostic index (PEPI) score Ki67 Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 INTRODUCTION Breast cancer (BC) is a complex and heterogeneous disease representing the most common malignancy among women worldwide [1]. BC comprises different clinical, histopathological and molecular subtypes. Those tumours expressing oestrogen receptor α (ER+) and not overexpressing human epidermal growth factor receptor-2 (HER2-) (hereafter referred to as ER+/HER2- BC) constitute the most common subtype, accounting for 65–70% of all breast tumours [2]. In early and advanced-stage disease, the cornerstone of its systemic treatment is endocrine therapy (ET) [3], usually administered, in the early setting, as adjuvant therapy (after surgery) for 5–10 years [4]. Although the use of neoadjuvant (before surgery) ET (NET) in ER+/HER2- BC disease is not fully implemented yet in the clinical practice, recent studies and clinical guidelines recommend NET in postmenopausal patients with low-risk ER+/HER2- tumours [5–9] as it is as effective as neoadjuvant chemotherapy (NACT), but with lower toxicity [10–13]. Similarly to other neoadjuvant therapies, NET also allows an in vivo evaluation of treatment sensitivity by monitoring of tumour response and offers the opportunity of interrupting inefficient or toxic therapies [14]. Moreover, post-NET clinical management (e.g. surgery planning or adjuvant therapy) can be based on data obtained during this treatment, allowing personalised strategies [15,16]. Despite all this, potential barriers condition the application of this therapeutic strategy in the clinical practice, as recently highlighted by experts in the field [10,17,18]. One of them is the lack of robust and reproducible biomarkers to assess response and long-term prognosis after NET. In 2012, the Food and Drug Administration (FDA) indicated that the complete pathologic response (pCR) could be used as a surrogate endpoint for neoadjuvant therapy [19]. However, it has been well-documented that pCR is not a suitable endpoint in the case of NET, mainly due to the low rate of pCRs (<5%) observed in this context [20–25]. Ki67 quantification [26,27] and the preoperative endocrine prognostic index (PEPI) score [28] are the main biomarkers currently available to evaluate prognosis after NET. Early changes in Ki67, while on NET treatment, have both a prognostic and predictive value [26]. However, Ki67 evaluation is not a reproducible parameter across laboratories (e.g. The International Ki67 in Breast Cancer Working Group (IKWG) and others do not recommend their use to guide routine clinical care [27,29,30]). Moreover, mPEPI and Ki67 are not independent parameters as PEPI calculation takes into account Ki67 levels, among others [28]). Nevertheless, NET is the perfect scenario for biomarkers research, as it allows the parallel study of diagnostic biopsies (pre-treatment) and their corresponding post-treatment surgical specimens, increasing its popularity as a highly relevant translational platform [17,31]. Indeed, some potential prognostic and/or predictive biomarkers and endpoints have been intensely debated in the last years [10,25,31–38]. In this context, our group has previously analysed tumour dynamics after NET in ER+/HER2- BC patients, describing that imaging techniques underestimate tumour size after NET and proposing a new biomarker of tumour response, called tumour cellularity size (TCS), that takes into account that NET response generates a diffuse-cell loss pattern [25]. This TCS lacks further prognostic validation. Here we searched for clinically relevant molecular reporters of NET response in a multicentre population of ER+/HER2- BC patients by using different molecular approaches: PAM50 based intrinsic subtype gene expression panel, protein evaluation of well-established key proteins involved in tumorigenesis and HER2-Low status evaluation. METHODS Study population This is a multicentric study with clinical data from 131 postmenopausal women with histologically confirmed, untreated, invasive, operable, non-metastatic ER+/HER2- BC treated with NET prior to surgery between 2005 and 2019. Data were prospectively collected and retrospectively analysed. Clinical characteristics of patients were similar independently of their hospital of origin. Except contraindicated, an aromatase inhibitor was the therapeutic option chosen for NET. Informed consent was obtained from all patients. The study was conducted in accordance with the Declaration of Helsinki and Good Clinical Practice guidelines as well as authorised by the Spain Health Authority and the local Ethics Committee. For the analyses, 3 representative and overlapping subcohorts were defined, as explained below. Gene expression data and subtyping according to PAM50 signature The gene expression-based PAM50 analysis was performed in matching diagnostic biopsies and surgical specimens (subcohort #1, n=58). For RNA purification, 10 μm formalin-fixed paraffin-embedded (FFPE) slides with a minimum of 100 ng of total RNA were used to measure the expression of the PAM50 panel through the nCounter platform (Nanostring Technologies, Seattle, WA, USA). Hematoxylin and eosin-stained slides from core biopsies and surgical specimens were examined to confirm the presence of tumour cells in the areas selected for this analysis. We employed the nSolver 4.0 Software to analyse gene expression, which was expressed as log-base 2 and normalised using 5 housekeeping genes ( ACTB , MRPL19 , PSMC4 , RPLP0 , and SF3A1 ). As previously described [40–43], Prosigna®-PAM50 predictor database was used to determine the intrinsic subtype of each sample. They were classified as: luminal A (LumA), luminal B (LumB), HER2-enriched (HER2-E), basal-like (basal) or normal-like (normal) intrinsic BC subtypes. This platform also allowed us to calculate the risk of recurrence (ROR) indexes: ROR-S (associated to the intrinsic subtypes gene set) and ROR-P (ROR index plus a proliferation-related index). Both scores classified patients as low-, intermediate- or high-risk, using the following pre-defined cut-off values (respectively, for ROR-S: 53; and for ROR-P: 53) [41]. We calculated the change in ROR-S (ΔROR-S) after NET as: ΔROR-S = [( ROR-S in surgery specimen) – ( ROR-S in baseline biopsy)] / |ROR-S in baseline biopsy|. According to this, an increase in ROR after NET implicates a positive value of ΔROR-S and a decrease a negative value in ΔROR-S. Moreover, the absolute value of ΔROR-S indicates the change magnitude. Histopathological analyses For all patients, we obtain FFPE diagnostic core biopsies and post- treatment surgical (after NET) specimens. The surgical specimens were examined by a pathologist to determine pathological tumour size (PTS; corresponding to the major diameter of the tumour in millimetres). In all samples (n=131), we determined the expression of oestrogen receptor (ER), progesterone receptor (PR) and Ki67 using international standards [44,45]. When possible, residual tumour cellularity (%) was evaluated and tumour cellularity size (TCS) calculated, as previously described [25]. Briefly, TCS is calculated as the product of the PTS multiplied by the percentage of epithelial cellularity and is expressed in millimetres. Modified PEPI (mPEPI) score was determined according to the characteristics of the surgical specimen, as previously published [28,46] (n=120). It includes PTS value, Ki67 levels and nodal status, and patients are classified into 3 risk groups with an associated differential risk of relapse. Immunohistochemistry (IHC) analyses of Bcl2, Sox2, p53, p21 and p16 protein levels were performed in core FFPE diagnostic biopsies and a tissue microarray (TMA) of surgical specimens (subcohort #2, n=61) by using the Ventana Roche system. Staining values for these biomarkers were expressed as i) the percentage of positive tumour nuclei, and ii) the change (∆ biomarker ) after NET was calculated as: ∆ biomarker = [( biomarker (%) in surgery specimen) – ( biomarker (%) in baseline biopsy)] / ( biomarker (%) in baseline biopsy). We also evaluated HER2 expression by IHC (Ventana System Roche) and following the most recent guidelines from American Society of Clinical Oncology/College of American Pathologists (ASCO/CAP) for HER2 testing [47–49]. We evaluated HER2 status in baseline biopsies and surgery specimens (subcohort #3, n=99). We complied with the terminology proposed by Denkert et al. in which tumours were considered as: 1) HER2-positive (HER2+) in case of IHC score 3+ and/or ERBB2 gene (encoding HER2) amplification by in situ hybridisation (ISH); 2) HER2-0 in case of IHC score zero; and 3) HER2-low in case of IHC scores 1+ or 2+, in the absence of gene amplification by ISH [36]; being HER2-0 and HER2-low tumours encompassed as HER2-negative (HER2-). Statistical analyses GraphPad Prism version 9 was used to carry out statistical analyses. For the descriptive statistical analyses, minimum, maximum, median and mean values were calculated for continuous variables. Wilcoxon matched-pairs, Mann Whitney and Kruskal-Wallis tests were performed to compare variables with non-Gaussian distributions. Chi-square or Fisher´s tests were used to determine differences between expected frequencies. Spearman´s r coefficients (rho) were used to evaluate correlations (with a 95% of confidence interval). p values < 0.05 were considered statistically significant. Unless otherwise specified, histograms represent mean values +/- standard error of the mean (SEM) and individual values are also included to visualise data dispersion. RESULTS Cohort description and patients’ distribution into subcohorts In total, data and samples from 131 patients with early ER+/HER2- BC were analysed. The main characteristics of the patients and tumours, their surgical management and the pathological changes after NET are summarised in Table 1 . The study population presented a mean age at diagnosis of 70 (47-93) and the mean NET duration before surgery was 9 months (2-40). Letrozole was the endocrine treatment administered in most cases (94.7%) and breast-conserving surgery was performed in 79% of the patients. Of note, one patient was diagnosed with bilateral disease, and her two tumours were independently considered in our analyses. Moreover, two patients achieved a pCR after NET (1.5% of patients) and, consequently, their post-NET surgical samples were not available for biological evaluation. Apart from these mentioned exceptions, matched samples from each patient were analysed (a core diagnostic biopsy or pre-NET sample, and the surgical specimen or post-NET sample). Suppl Mat Fig S1 shows the distribution of patients in 3 subcohorts and the different analyses performed in each of them. The distribution of patients into each subcohort took into account the sample availability (e.g. location) and sample quality required for each analysis. Our subcohorts are representative of the general cohort as observed in Suppl Mat Tab 1 . PAM50 analysis (intrinsic subtype and ROR score) in core biopsies can predict NET response The 50 gene expression-based PAM50 assay allows the classification of BC into 5 intrinsic biological subtypes and generates risk of recurrence (ROR) scores for each sample [39]. However, the utility of PAM50 to predict treatment benefit requires further investigation in the specific context of NET. In this regard, we selected a representative subcohort of ER+/HER2- BC patients treated with NET (subcohort #1 in Suppl Mat Fig S1 ) and we obtained the PAM50 data (intrinsic subtype and ROR scores) on their diagnostic biopsies (pre-NET) and surgery specimens (post-NET) ( Table 2 ). As expected, due to the characteristics of our cohort, low number of patients presented HER2-E or basal subtypes. First, to check if our subcohort behaved similarly to others of this kind, we validated previous findings regarding the predictive value of Luminal A (LumA) and Luminal B (LumB) baseline PAM50 categorization in ER+ BC treated with NET [33]. As expected, our results showed that LumA presented lower Ki67 after treatment, ΔKi67 and mPEPI score than LumB, indicating a better biological response to NET and better prognosis ( Figure 1A-B, Suppl Mat Fig S2A ). Moreover, to add further evidence of the predictive value of PAM50 evaluation at baseline (pre-NET), we analysed ROR score data. We observed that tumours with high ROR scores had worse response to NET evaluated as higher Ki67 % at surgery and a lower change in Ki67 (measured by ΔKi67) together with a worse prognosis with a higher mPEPI score ( Figure 1C-D, Suppl Mat Fig S2B and Table 2 ). Moreover, tumour cellularity size (TCS), a novel biomarker for NET response described for the first time by Lopez-Velazco et al. [25] correlates with ROR-S at baseline (pre-NET) ( Figure 1E ). No differences were found for ROR-S and ROR-P ( Suppl Mat Fig S2C-F ). These results support that PAM50 analysis in the diagnostic biopsy could help to personalize the use of NET, conjointly with other clinical parameters. Conversion from Luminal A to Normal-like intrinsic subtype after NET is associated with better response As mentioned above, NET is a therapeutic strategy that allows the characterisation of tumour response by comparing diagnostic biopsies (pre-NET) and surgical specimens (post-NET) [17,31]. Nowadays, limited data exist regarding intrinsic subtype changes in paired pre- and post- NET samples of ER+/HER2- BC patients. That is why we characterised the changes in the intrinsic subtype upon NET in matched samples and analysed their relationship to treatment response. The distribution of PAM50-based intrinsic subtypes among pre- and post-NET samples and the value of the main NET biomarkers of each change group are described in Table 2, Figure 2 and Suppl Mat Fig S3A-C . Our results showed that the two most represented profiles of intrinsic subtype after NET were: the group of tumours with a persistent LumA status (36% of our patients) and the group of tumours with a conversion of LumA to Normal-like status upon therapy (26% of patients). We wondered whether there could be a difference in response/prognosis to NET between these two groups which share the LumA pre-NET status. Our results showed that tumours changing from LumA to Normal-like after NET presented: lower Ki67 levels at surgery, a significant decrease in Ki67 (ΔKi67), lower mPEPI score and lower TCS ( Figure 2C-E, Suppl Mat Fig S3D ). Importantly, Lopez-Velazco et al. [25] proposed a cutoff for TCS at 2.5mm. We found that 92% of Persistent LumA tumours presented a high TCS (>2.5mm), while LumA to normal subgroup was enriched in low TCS (≤ 2.5 mm) tumours ( Figure 2F ). Later, we assessed whether these results could be conditioned by the tumour size or epithelial cellularity content. Our analyses showed that our two populations of interest did not statistically differ in these parameters although a tendency is observed regarding to cellularity ( Figure 2G-H ). Next, we analysed if the change in ROR may also help us to evaluate NET response and prognosis. We observed that, in our cohort, ROR values are significantly lower after NET compared to the ones obtained pre-NET ( Table 2, Figure 3A, Suppl Mat Fig S3E ). Interestingly, Figure 3A shows that none of tumours changing from Luminal A (LumA) to normal-like (normal) intrinsic subtype increases its ROR after NET. The potential of ROR change (∆ROR) between pre- and post-NET samples as a marker of NET response and prognosis has not been largely explored in previous studies. Our results indicate that ∆ROR value should be taken into consideration, at least in the case of ROR-S. First, we plotted ∆ROR-S values from all patients ( Figure 3B ) and found 3 subpopulations: i) ∆ROR-S ≥ 0 (including patients with no change or increase of ROR-S after NET; 14 out of 59); ii) 0>ΔROR-S>-2.5 (including patients with a slight decrease in ROR after NET; 31 out of 59); and iii) ΔROR-S≤-2.5 (including patients with an accentuated change in ROR after NET; 14 out of 59). ΔROR-S values are significantly different between ii) and iii) according to statistical analyses, indicating that a cut-off at ΔROR-S = -2.5 could stratify two subpopulations of patients. At this point, we wondered whether these two subpopulations might show differences in their NET response and prognosis. Statistically significant differences in Ki67, ΔKi67 and mPEPI between these novel subgroups of patients subdivided taking into account their ΔROR-S values ( Figure 3C-E ). Interestingly, subgroups ii) and iii) present clear differences, being subgroup ii) more similar to i) than to iii). TCS results showed a trend to lower TCS in the iii) group, although no statistically significant was reached ( Figure 4F ) likely due to insufficient number of patients per group. Thus, this novel stratification of patients into 3 subgroups according to their changes in ROR-S (ΔROR-S, establishing cut-off at ΔROR-S=0 and ΔROR-S=-2.5) has revealed relevant differences in NET response and prognosis between patients depending on the magnitude of their ROR decrease after NET. In summary, our data indicate that analysing the change in the intrinsic subtype or ROR value upon NET can provide information about response to NET and patient prognosis that could help in the clinical practice to, for example, determine adjuvant therapy. Quantification of p53 levels before and after NET may contribute to asess NET response Previous studies indicate that biological markers (e.g. senescence or apoptosis markers) may be useful to determine the efficacy of a neoadjuvant therapy (mainly, chemotherapy) in BC patients [50–53]. However, few data are published in the NET setting in ER+/HER2- BC. Here, we study the protein levels of different biomarkers of apoptosis (Bcl2), cancer stem cell phenotype (Sox2) and senescence (p53, p21 and p16) by IHC in matched pre- and post-NET samples as well as the value of their change after NET. For that, we selected a novel subcohort (subcohort #2 in Suppl Mat Fig S1 ), representative of our global cohort of ER+/HER2- BC patients treated with NET, composed of 61 patients. The quantifications of the mentioned markers in each sample type are shown in Figure 4A and Suppl Mat Fig S4A-D . The percentage of positive cells for p53 and p21, two senescence markers, showed a clear decrease after NET (FC≥-2), while the percentage of positive cells for Bcl2 (apoptosis marker) presented also a significant decrease after NET but the magnitude change was lower (FC=-1.1). To determine the predictive/prognostic value of these markers, we compared them (pre-NET, post-NET or changes) with the validated predictive NET biomarkers (Ki67 expression at surgery, ∆Ki67 and with the prognostic mPEPI score). These analyses were performed with all the markers, although Sox2, Bcl2 and p16 did not show any association (data not shown). First, our results showed that, in the pre-NET samples, only p53 might have predictive capacity since a higher expression was associated with higher Ki67 and mPEPI grading, indicating poor response and prognosis after NET ( Figure 4B-C ). Next, we studied the protein levels of the mentioned markers in our post-NET samples. We observed that the expression of p53 and p21 was inversely associated with response to NET ( Figure 4D-E and Suppl Mat Fig 5A-B ). Finally, we decided to study if the change in the mentioned markers before and after NET may be related to NET response. Importantly, we found that the change in levels of p53 (∆p53) after NET may be associated to response/prognosis due to their correlation with mPEPI score and TCS (cutoff at 2.5mm) ( Figure 5F-G ). The change in levels of p21 (∆p21) after NET may be also associated to NET response according to its correlation with Ki67 ( Suppl Mat Fig S5C ). However, further validation is needed to establish their value as long-term prognostic biomarkers . In summary, in the search for new biomarkers of response with long-term prognostic value upon NET, our results suggest that, among another biological biomarkers evaluated, the percentage of positive cells for p53 (assessed in pre-NET and post-NET samples and its change) showed promising results in terms of its ability to characterise residual cell populations with prognostic interest, deserving this further validation. A change in HER2 status is observed after NET, although it is not associated to tumour response HER2-low status has gained huge attention during the last years in the field due to the fact that some studies point out that it should be considered as an independent biologic subtype distinct from HER2-0 BC, although its clinic implications are not well-elucidated [49,54]. Here, we explored the relationship of HER2-low status with NET response and prognosis. For that, we defined a novel subcohort (subcohort #3 in Suppl Mat Fig S1 ), representative of our global cohort of ER+/HER2- BC patients treated with NET, composed of 99 patients. We collected retrospectively the patients’ data related to HER2 in their diagnostic biopsies (pre-NET) and surgical specimens (post-NET) ( Table 3 and Figure 5A-B ). First, we studied if subdividing pre-NET samples into HER2-0 and HER2-low may predict NET response. We observed that, at diagnosis, pathologists’ analyses indicated that most of our patients could be classified as HER2-low (HER2-low: n=66; HER2-0: n=33), but we did not find any relationship between HER2 status and NET predictive/prognostic markers (Ki67, mPEPI and ∆Ki67; Suppl Mat Fig 6A-C ). Similarly, when we analysed if the stratification of post-NET samples into HER2-0 and HER2-low groups may be related to Ki67 levels and PEPI score, we did not obtain any positive result (HER2-low: n=32; HER2-0: n=62) ( Suppl Mat Fig 6D-F ). Importantly, by comparing HER2 status between pre- and post-NET samples ( Figure 5A-B ), we observed a statistically significant decrease in HER2 scoring after NET and, consequently, enrichment in HER2-0 samples ( Suppl Mat Fig 7A ). Around half of the samples presented a discordant pre- vs post-NET HER2 status (40 of 94 patients (43%) when comparing HER2-0 vs HER2-low; Table 3 ). In detail, 36 patients (38%) changed their HER2 status from HER2-low (pre-NET) to HER2-0 (post NET) ( Suppl Mat Fig 7A and Table 3 ), being this the most prevalent change observed in our cohort. Thus, we checked whether this change may be used as a marker of response to NET (compared to HER2-0 and HER2-low persistent patients), but we did not observe any relationship with the aforementioned NET prognostic/predictive markers ( Suppl Mat Fig 7B-D ). We explored other analyses, comparing different groups, but we did not obtain any positive relationship between HER2 status change after NET and NET response (data not shown). Taken together, the results of our ER+/HER2- cohort indicate that, after NET, a generalized decrease in HER2 status can be observed but it is not associated to NET response. Analysing pre-NET and post-NET data individually neither supports the interpretation of HER2-low status as a distinct biologic entity in the context of NET response in ER+/HER2- patients. DISCUSSION Successful treatment of early breast cancer relies, nowadays, on an optimal combination of a minimally invasive local therapy (i.e. surgery and radiotherapy) and a personalized systemic therapy that maximizes reduction of risk of relapse and minimizes toxicity [31,55]. Until recently this optimization of systemic therapy was merely based in basal risk of relapse being more toxic therapies (e.g. chemotherapy or prolonged endocrine therapy) justified when risk of relapse was higher as anatomically defined by node involvement or larger tumor size, for example [56]. Current development of new systemic therapies in the adjuvant setting beyond chemotherapy or anti-estrogens has boosted the need for an individualized approach that, overcoming mere risk-based sub-population definition, includes sensitivity to the different therapies as a cornerstone of patient-based decision. Out of the three major subtypes of BC, namely ER+/HER2-, HER2 amplified and triple negative, only patients with the two latter have, available, an individual-sensitivity approach. This way, patients with HER2 amplified tumors will be routinely exposed to chemotherapy plus a double anti-HER2 antibody treatment prior to therapy and, then, depending if pCR is achieved or not, exposed to either antibodies or a combination of antibodies with chemotherapy in the post-surgical therapy. Similarly, the post-surgical therapy of triple negative patients will vastly depend on the achievement of a complete response after primary systemic therapy pre-surgery [7,57]. Although the above-mentioned advances in personalization of therapies have been made possible in the least populated subgroups of BC patients, those with ER+/HER2- tumors, the vast majority, lack this possibility. This unmet need is becoming particularly urgent when new therapies (e.g. CDK 4/6 inhibitors) are being introduced in the adjuvant setting of these patients without a real guide for individual sensitivity selection of patients and still relying in mostly anatomical risk factors [58]. The most relevant breakthrough in treatment de-escalation in luminal patients has come through the introduction of the polygenic platforms that assess the risk of relapse in a more biology-based way but are still vastly depending in risk of relapse assessment [59,60]. Although it is well known that ER+/HER2- BC patients are, as a group, those benefiting less from toxic adjuvant chemotherapy, to date, no consensus has been reached on how to robustly evaluate the benefit from the cornerstone systemic therapy on these patients, namely endocrine therapy, in the pre-operative setting so the decision on post-surgical systemic therapy (e.g. chemotherapy, CDK 4/6 inhibitors, prolonged and/or combined antiestrogen therapy) is facilitated [61,62]. And this is mainly due to the scarcity of pCR following NET. Four major considerations should be beard in mind when searching for reproducible, valid markers of response assessment to NET. First, any method of assessment needs to be developed following biomarkers standard procedures, including, in particular in this setting, validation in its ability to predict long-term outcomes. Second, currently available biomarkers in this setting, vastly depend on Ki67 which is, as discussed, far from being a reproducible biomarker [29,30,63,64]. Third, multiple gene-expression platforms have demonstrated high utility and reproducibility in ER+/HER2- BC both for molecular subtype and prognosis assessment [32,60]. And fourth, pathologic response has a particular scattered pattern in these tumours, that usually does not include complete response, but with marked tumor cell population reduction [22,25,65–67]. Hence it looks logical to seek for robust, reproducible, NET sensitivity reporters, among already available multigene expression panels, on the one side, and to produce a robust system of pathologic assessment of response beyond those developed for primary chemotherapy. A disadvantage of most studies covering this scientific question is that they are usually focused on one biomarker and they do not combine different analyses in the same cohort. This makes it difficult to comparatively analyse the different proposed biomarkers. In this and our previous work we propose, coinciding with other groups, two markers that could be considered strong candidates for further validation as reporters of optimal/suboptimal response to NET, namely, the change into a Normal-like subtype, and the Tumor Cellularity Score [25,68,69]. As a clear neat trait of our series, that might be considered a limitation, we have developed the study in a series of small tumours with good favorable features. Our cohort characteristics are similar to others treated with NET [21,23,26,70]. As in our, those cohorts were composed by patients with small tumours (<2cm evaluated by USS) with low rate of positive lymph node at diagnosis (confirmed by node biopsy) and with a low rate of pCR (<5%) after therapy. Previous evidence about PAM50 subtypes in baseline and residual tumours following NACT plus Trastuzumab in HER2 positive BC showed that after therapy, the two principal changes in PAM50-based intrinsic subtype were an increase in Luminal A and Normal-like subtypes [71]. In the NET context, Gil Gil and collaborators [72] analysed PAM50 profile in surgical specimens from patients of a retrospective study of 119 postmenopausal women with HR-positive stage II-III BC treated with NET. The PAM50 subtype distribution after NET was Luminal A 54.3%, Normal-like 24.3%; HER2-enriched 16,5%, Luminal B 1% and basal 1%. These results are in line with our study in which the two more frequent PAM50 subtypes after NET were Luminal A and Normal-like in 28 (48%) and 19 (33%) patients, respectively. In a substantially similar study to ours Schettini et al [68] have reported a conversion rate to Normal-like 8% pre to 42% post NET. This group, additionally, provide the first evidence on the good long-term prognostic significance that this conversion may have. Although a lower percentage of tumor cells in the post-NET sample may contribute to the normal expression profile, it cannot be the only factor since, statistically, our samples pre- and post-NET, do not differ in epithelial cellularity. This change to Normal-like subtype has a clear additional value to the diagnostic phenotype were this subtype is rare. This is particularly relevant bearing in mind that those tumours with a Luminal A subtype after primary therapy still beard a considerable risk of relapse as shown in a study on patients treated with NACT by Denkert et al [73], who reported how patients with a Lum A profile at surgery still beard a substantial risk of relapse with an invasive Disease Free Survival rate of 79,5% at 3 years. This way we hypothesized that, beyond the initial better prognosis of Luminal A patients at diagnosis, a conversion to Normal-like after NET may characterize a population of extremely good prognosis that could be considered for de-escalation trials. We have shown that those tumours changing from Luminal A to Normal-like after NET presented lower Ki67-stained cells at surgery, a significant decrease in Ki67 (ΔKi67) and lower mPEPI score. Additionally, we found, using our recently described TCS as a mean to comprehensively capture the prognostic value of the tumor size and the effect of NET on cellularity, that tumors converting to Normal-like had a neat lower TCS than those remaining Luminal A. Moreover, current studies are describing the changes in gene expression assays (such as Oncotype or PAM50) observed after NST [74]. Such is the case of Pascual et al [75] who described that in the CORALLEEN trial (N=106, Luminal B early BC treated with NST), the PAM50 proliferation score significantly decreased after Ribociclib plus Letrozole and after NACT between baseline and time of surgery for samples with a complete cell cycle arrest (CCCA, KI67<2.7%) and non-CCCA samples (p<0.001). Acknowledging the clear limitations of the use of ROR scores in post-NET samples where there is no evidence at all of their prognostic value, we hypothesized that the ROR-score ROR-S, which is less sensitivity to proliferation abolition than ROR-P and more dependent on subtype discrimination, might help to characterize in a dynamic way the prognostic implications of the change in tumour characteristics after NET. We demonstrate that the ΔROR-S has a clear correlation with the currently available reporters of response and good prognosis after NET (ΔKi67 and mPEPI score). What is more, virtually no patient with a tumor converting from Luminal A to Normal-like lacked a decreased in ΔROR-S which is, in our view, an extremely encouraging feature of the change in subtype as a landmark of extremely good prognosis characterization. Ueno et al. and Hilal et al. published recently [76,77] an initially contradictory result to ours with regard to ROR post-NET performance in terms of prognostic prediction. To put that into context it should be recognized that, firstly, these authors used OncotypeDx® based ROR, a different technology much more likely to be influenced by the proliferation abolition after NET than ROR-S score, which is enriched in subtype specific genes. Additionally, we calculated ΔROR normalizing by basal levels, which differs from this other study where this normalization was not taken into account. In a much more exploratory aspect of our study, we analysed the relationship between NET and apoptosis and senescence-associated markers. We investigated as to whether the lack of pCR, characteristic of NET, which can be attributed at least partly to a reduction of apoptosis paralleling the reduction in proliferation, can be captured by the change in markers of apoptosis and, so, used to characterize patterns of response. Out of our investigation we found that p53 levels (pre- and post-NET) and its change after therapy (decrease) are clearly related to NET. Interestingly, these results are in line with those observed by Mueller et al, [35]. In their study p53 IHC staining decrease after NET and they described a relationship between this change with response to therapy in terms of Ki67 index. Also, as ours, their results indicated that pre- and post p53 levels per se, are related to response to NET and that those patients with poorer response presented higher levels of p53 in both evaluations. Approximately 80% of BC not overexpressing HER2 are currently defined as HER2-negative [49]. However, half of these BC show some degree of HER2 expression by IHC and are currently defined as HER2-Low. Several studies, boosted by the current availability of HER2-Low targeted therapies [54,78,79] have shown conflicting results and, currently, it is highly unclear whether HER2-Low BC should be considered an individual biologic subtype distinct from HER2-0 BC [54]. The biology and predictive-prognostic implications of a HER2-Low BC are not yet well-elucidated and some studies in the neoadjuvant setting (mainly in chemotherapy) in ER+/HER2- BC have shown contradictory results when comparing pCR rates between HER2-Low and HER2-0 status (in pre-treatment diagnostic biopsy) [36,78,80–82]. In this context, our results do not endorse a differential behavior of HER-2 low versus HER-2 0 tumors but highlight the need to re-test HER2 after NET on the basis of the significative incidence of HER2 expression loss. As a shortcoming of our results to this extent we highlight that our HER2 results were assessed before the current awareness of pathologists on the importance of the differentiation between HER2 low and HER2 0 in the HER2 negative population. As a final conclusion we considered that the present results, along with those of other groups, uncover the intrinsic subtype change after NET, in particular the conversion to Normal-like, as a putative biomarker characterizing a population that benefit highly from endocrine therapy. If considered conjointly with the pathologic evaluation of response via reproducible methodology as might be the TCS assessment, we could easily characterize that population benefiting most from endocrine approach and bearing a potential very good prognosis. These two biomarkers should be validated in independent series including long-term outcomes. If confirmed in their value, the population they characterize should be considered optimal for de-escalation trials allowing NET to become a real tool for treatment personalization in ER+/HER2 negative early breast cancer. Declarations ADDITIONAL INFORMATION Supplementary information is available at Breast Cancer Research journal website. ACKNOWLEDGEMENTS We are grateful to the members of our laboratory for critical discussion of this work, and, for their technical assistance, to the Pathology Services of: i) OSI Donostialdea – Onkologikoa (San Sebastián, Spain), ii) Hospital Clínico Universitario de Valencia (Valencia, Spain), iii) Valencia Oncology Institute (Valencia, Spain), iv) Catalan Institute of Oncology (Badalona, Spain), v) Hospital October 12 (Madrid, Spain), and vi) Hospital Clinic – Barcelona – IDIBAPS (Barcelona, Spain). ETHICS APPROVAL The study was conducted in accordance with the Declaration of Helsinki and Good Clinical Practice guidelines as well as authorised by the Spain Health Authority and the local Ethics Committee. AUTHOR CONTRIBUTIONS JILV, SM, AU and MMC analysed the data and wrote the manuscript. AU designed the study. AU and MMC co-supervised the study. KE performed the histopathological analyses. JILV, SM, MO, AL, LA, IE, MH, EB, JG, VQ, MF, SA, IA and AU collected and examined patient data. LP and AP performed PAM50 assay. All authors reviewed the manuscript. FUNDING This work was funded by Instituto de Salud Carlos III (ISCIII) grants: PI21/01208, PI20/01253, CP18/00076 and FI19/00193 co-funded by the European Union, Basque Department of Health (2020111040), Fundación SEOM (SEOM Avon Fellowship 2020) and Ikerbasque Basque Research Foundation. The group also received funds from the breast cancer patient’s charity Katxalin and from Roche Farma S.A. 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Oxford University Press; 2022. p. 328–36. Miglietta F, Griguolo G, Bottosso M, Giarratano T, Lo Mele M, Fassan M, et al. HER2-low-positive breast cancer: evolution from primary tumor to residual disease after neoadjuvant treatment. npj Breast Cancer. 2022;8:1–7. Kang S, Lee SH, Lee HJ, Jeong H, Jeong JH, Kim JE, et al. Pathological complete response, long-term outcomes, and recurrence patterns in HER2-low versus HER2-zero breast cancer after neoadjuvant chemotherapy. Eur J Cancer [Internet]. 2022;176:30–40. Available from: https://doi.org/10.1016/j.ejca.2022.08.031 Schettini F, Chic N, Brasó-Maristany F, Paré L, Pascual T, Conte B, et al. Clinical, pathological, and PAM50 gene expression features of HER2-low breast cancer. npj Breast Cancer [Internet]. 2021;7. Available from: http://dx.doi.org/10.1038/s41523-020-00208-2 Tables Table 1. Tumour characteristics. Pre-NET diagnostic biopsies (n=132*) Post-NET surgical resections (n=132ª) p-value Grade [n (%)] I 27 (20) 39 (29) ns II 99 (75) 89 (67) III 6 (5) 2 (2) N/A 0 2 (2) Histology [n (%)] NST 110 (83) 104 (79) ns ILC 12 (9) 15 (11) Other special type 10 (8) 11 (8) N/A 0 2 (2) T state [n (%)] cT ypT T1 52 (39) 80 (61) T2 69 (52) 46 (35) T3 10 (8) 3 (2) N/A 1 (1) 3 (2) N state [n (%)] cN ypN Negative 109 (82) 83 (63) Positive 22 (17) 40 (30) N/A 1 (1) 9 (7) Positive cells (%) [mean (range)] Ki67 21 (1-80) 10 (1-80) <0.0001 Oestrogen receptor 93 (8-100) 90 (0-100) 0.02 Progesterone receptor 62 (0-100) 17 (0-99) <0.0001 *One patient was diagnosed with bilateral disease, and her two tumours were independently considered. ªTwo tumours were not evaluable for biological characteristics at surgery because the patient achieved a pCR. c/yp tumour (T) status was determined clinically (by RMN or USS) and pathologically before and after NET.c/yp axillary node status (N) was determined clinically (by USS) and pathologically before and after NET N/A: not available, NST: no special type, ILC: invasive lobular carcinoma, ns: non-significant. Chi-square or Wilcoxon matched-pairs signed rank tests performed. Table 2. PAM50-intrinsic subtype profiles and ROR scores in pre and post NET samples. % Ki67 expression at surgery, ∆KI67 and mPEPI score groups are included in the table. ROR-S and P scores are shown as L: low, M:medium or H:High. Intrinsic subtype n (%) ROR-P score (pre-NET) ROR-S score (pre-NET) Ki67 + cells (%) at surgery ∆Ki67 mPEPI score groups (n)* Pre-NET Post-NET L M H L M H % mean (range) mean (range) 0 1-3 >3 LumA LumA 21 (36) 5 16 0 13 8 0 8 (1 - 27)* -0,41 (-0,95 to 1)* 1 8 5 LumA Normal 15 (26) 9 6 0 14 1 0 2 (0 - 20) -0,83 (-0,05 to -1) 9 5 1 LumA LumB 3 (5) 0 3 0 2 1 0 12 (1-18) -0,56 (-0,6 to -0,97) 0 0 3 LumA HER2-E 1 (2) 0 1 0 0 1 0 60 1 0 0 1 LumB LumA 6 (10) 0 3 3 0 4 2 23 (5 - 50) 0,12 (-0,64 to 1,25) 0 3 3 LumB Normal 3 (5) 0 2 1 0 3 0 8 (6 - 10) -0,6 (-0,6 to -0,7) 0 2 0 LumB LumB 5 (9) 0 1 4 0 3 2 38 (22 - 45)* -0,23 (0 to -0,44)* 0 0 4 HER2-E HER2-E 2 (3) 0 0 2 0 0 2 34 (28 - 39) 0,23 (0,16 to 0,30) 0 1 1 HER2-E LumA 1 (2) 0 1 0 0 1 0 22 0,22 0 1 0 Normal Normal 1 (2) 1 0 0 1 0 0 1 -0,9 1 0 0 Total [n (%)] 58 (100) 15 33 10 30 22 6 11 20 18 *Not all values were available for all patients. Table 3. Retrospective analysis of HER2 status in our ER+/HER2- cohort of patients treated with NET. Pre- and post-NET HER2 status. Pre-NET n % Post-NET n % HER2-0 33 33 HER2-0 62 66 HER2-low 66 67 HER2-low 32 34 Total 99 100 Total 94 100 HER2-low +1 41 62 HER2-low +1 20 63 +2 25 38 +2 11 34 +3 0 0 +3 1 3 Total 66 100 Total 32 100 HER2, human epidermal growth factor receptor 2. Additional Declarations No competing interests reported. Supplementary Files LopezVelazcosuppmaterial.pdf Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4310954","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":296750601,"identity":"48572679-b675-45d3-b33c-7f6c9df208f3","order_by":0,"name":"Joanna I. López-Velazco","email":"","orcid":"","institution":"Biogipuzkoa Health Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Joanna","middleName":"I.","lastName":"López-Velazco","suffix":""},{"id":296750602,"identity":"7912211f-d1b5-47b8-b295-e9ea2fc66d48","order_by":1,"name":"Sara Manzano","email":"","orcid":"","institution":"Biogipuzkoa Health Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Sara","middleName":"","lastName":"Manzano","suffix":""},{"id":296750603,"identity":"1f940dab-8ab8-48ab-b832-586fcd6f5a5d","order_by":2,"name":"Kepa Elorriaga","email":"","orcid":"","institution":"OSI Donostialdea – Onkologikoa","correspondingAuthor":false,"prefix":"","firstName":"Kepa","middleName":"","lastName":"Elorriaga","suffix":""},{"id":296750604,"identity":"c9510559-6d75-4705-ac06-c4927470b21c","order_by":3,"name":"Maria Otaño","email":"","orcid":"","institution":"OSI Donostialdea – Onkologikoa","correspondingAuthor":false,"prefix":"","firstName":"Maria","middleName":"","lastName":"Otaño","suffix":""},{"id":296750605,"identity":"5613e042-45a3-4946-a271-ea1feb5bcd1c","order_by":4,"name":"Ainhara Lahuerta","email":"","orcid":"","institution":"Biogipuzkoa Health Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Ainhara","middleName":"","lastName":"Lahuerta","suffix":""},{"id":296750606,"identity":"81e1f776-62b4-4067-a87f-23488eb3fca1","order_by":5,"name":"Luis Álvarez","email":"","orcid":"","institution":"Biogipuzkoa Health Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Luis","middleName":"","lastName":"Álvarez","suffix":""},{"id":296750607,"identity":"84f27063-677c-4b12-865f-daab67d4d08e","order_by":6,"name":"Inge Etxabe","email":"","orcid":"","institution":"OSI Donostialdea – Onkologikoa","correspondingAuthor":false,"prefix":"","firstName":"Inge","middleName":"","lastName":"Etxabe","suffix":""},{"id":296750608,"identity":"bbc4c225-9c9a-4817-8a8f-5e8c516ae973","order_by":7,"name":"Miren Huarte","email":"","orcid":"","institution":"OSI Donostialdea – Onkologikoa","correspondingAuthor":false,"prefix":"","firstName":"Miren","middleName":"","lastName":"Huarte","suffix":""},{"id":296750610,"identity":"c80de17b-6921-471e-8937-f73630b3b81e","order_by":8,"name":"Elvira Buch","email":"","orcid":"","institution":"Hospital Clínico Universitario de Valencia","correspondingAuthor":false,"prefix":"","firstName":"Elvira","middleName":"","lastName":"Buch","suffix":""},{"id":296750612,"identity":"63540272-2e49-41b4-a5ba-84a6f3fb032c","order_by":9,"name":"Julia Gimenez","email":"","orcid":"","institution":"Valencia Oncology Institute","correspondingAuthor":false,"prefix":"","firstName":"Julia","middleName":"","lastName":"Gimenez","suffix":""},{"id":296750623,"identity":"eebdb162-6f5e-4fa0-b4ad-d7ac30c0ab2e","order_by":10,"name":"Vanesa Quiroga","email":"","orcid":"","institution":"Catalan Institute of Oncology","correspondingAuthor":false,"prefix":"","firstName":"Vanesa","middleName":"","lastName":"Quiroga","suffix":""},{"id":296750624,"identity":"5ce1439b-d282-46fa-8558-2e1f026651c3","order_by":11,"name":"Marta Fernandez","email":"","orcid":"","institution":"Biogipuzkoa Health Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Marta","middleName":"","lastName":"Fernandez","suffix":""},{"id":296750625,"identity":"8e1cbe86-cbf5-415e-9c97-1d5858e130eb","order_by":12,"name":"Sofía Aragón","email":"","orcid":"","institution":"Hospital October 12","correspondingAuthor":false,"prefix":"","firstName":"Sofía","middleName":"","lastName":"Aragón","suffix":""},{"id":296750626,"identity":"a66fcbe0-b795-4425-b0e0-4a2fc8204a09","order_by":13,"name":"Laia Paré","email":"","orcid":"","institution":"Hospital Clinic, Barcelona – IDIBAPS","correspondingAuthor":false,"prefix":"","firstName":"Laia","middleName":"","lastName":"Paré","suffix":""},{"id":296750628,"identity":"d7050a7b-9592-4f7e-be16-a3a155c9f63b","order_by":14,"name":"Aleix Prat","email":"","orcid":"","institution":"Hospital Clinic, Barcelona – IDIBAPS","correspondingAuthor":false,"prefix":"","firstName":"Aleix","middleName":"","lastName":"Prat","suffix":""},{"id":296750630,"identity":"80417b37-0d67-411b-9462-5e1c482ee96e","order_by":15,"name":"Isabel Alvarez","email":"","orcid":"","institution":"Biogipuzkoa Health Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Isabel","middleName":"","lastName":"Alvarez","suffix":""},{"id":296750638,"identity":"a590d106-bc11-4217-b4a0-840e4df4f5ab","order_by":16,"name":"Maria M. Caffarel","email":"data:image/png;base64,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","orcid":"","institution":"Biogipuzkoa Health Research Institute","correspondingAuthor":true,"prefix":"","firstName":"Maria","middleName":"M.","lastName":"Caffarel","suffix":""},{"id":296750641,"identity":"f4339f6b-b77d-4fc4-b583-54add605214c","order_by":17,"name":"Ander Urruticoechea","email":"","orcid":"","institution":"Biogipuzkoa Health Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Ander","middleName":"","lastName":"Urruticoechea","suffix":""}],"badges":[],"createdAt":"2024-04-23 09:37:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4310954/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4310954/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":55634496,"identity":"11419076-996e-4a09-9305-537acd25672c","added_by":"auto","created_at":"2024-04-30 20:10:51","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":179107,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePAM50 analysis in pre-treatment core biopsies has predictive value in the context of NET.\u003c/strong\u003e \u003cstrong\u003e(A-B)\u003c/strong\u003e Association between Luminal A (Lum A) and Luminal B (Lum B) intrinsic subtypes in pre-NET samples with percentage (%) of Ki67 positive (+) cells at surgery (A) and mPEPI score (B). \u003cstrong\u003e(C-E)\u003c/strong\u003e Association between ROR-S score in pre-NET samples with percentage (%) of Ki67 positive (+) cells at surgery (C), mPEPI score (D) and Tumour cellularity size (TCS, mm, E). Med = medium. Mann-Whitney test p value is show when two groups comparison was calculated. Spearman correlation coefficients (rho) and p values are shown for correlation analyses (E).\u003c/p\u003e","description":"","filename":"BCRLopezetalMainFigures1.png","url":"https://assets-eu.researchsquare.com/files/rs-4310954/v1/c6d5434f81fc35b70fd123ec.png"},{"id":55634495,"identity":"fe1fcfc1-e9fe-4312-90a9-2f7e80dd4bae","added_by":"auto","created_at":"2024-04-30 20:10:50","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":499251,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociation of changes in PAM50-intrinsic subtype after NET with response to therapy.\u003c/strong\u003e \u003cstrong\u003e(A)\u003c/strong\u003e Alluvial diagram showing the changes in the PAM50-intrinsic subtype observed in pre- and post-NET matched samples. \u003cstrong\u003e(B) \u003c/strong\u003ePie charts representing the PAM50-intrinsic subtypes distribution in pre-NET (upper panel) and post-NET (lower panel) samples.\u003cstrong\u003e (C-H)\u003c/strong\u003eAssociation between changes in intrinsic subtype (persistent Luminal A (Persistent LumA) status or change from Luminal A to Normal-like status (LumA to Normal)) with percentage (%) of Ki67 positive (+) cells at surgery (C), mPEPI score (D), tumour cellularity size (TCS, mm) in absolute values (E) or in high or low TCS subgroups (F), tumour cellularity (%, G) and pathological tumour size (PTS, H). Luminal = Lum, → = change, HER2-E = HER2-Enriched.\u003c/p\u003e","description":"","filename":"BCRLopezetalMainFigures2.png","url":"https://assets-eu.researchsquare.com/files/rs-4310954/v1/41d48e81d9e75c026706a6c6.png"},{"id":55634494,"identity":"a58e6e10-e018-4574-991b-1999bd802cc7","added_by":"auto","created_at":"2024-04-30 20:10:50","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":237299,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociation of changesin ROR after NET with response to therapy. (A)\u003c/strong\u003e ROR-S values pre- and post-NET, indicating if samples belong to persistent Luminal A (LumA), change from Luminal A to normal-like (LumA to normal) or others, according to the colour legend included in the right side. \u003cstrong\u003e(B)\u003c/strong\u003e ΔROR-S values and subgroups (blue, green and red) definition considering cut-offs at: ΔROR-S = 0 (biological cut-off) and ΔROR-S=-2.5 (novel cut-off). \u003cstrong\u003e(C-F)\u003c/strong\u003e Association analysis between tumours that belong to ΔROR-S groups i), ii) and iii) with percentage (%) of Ki67 positive (+) cells at surgery (C), ΔKi67 (D), mPEPI score (E), and tumour cellularity size (TCS, mm, F). Subgroups i to iii) include tumours with ∆ROR-S ≥0 (subgroup i), with 0\u0026gt;ΔROR-S\u0026gt;2.5 (subgroup ii) and with ΔROR-S≤2.5 (subgroup iii).\u003c/p\u003e","description":"","filename":"BCRLopezetalMainFigures3.png","url":"https://assets-eu.researchsquare.com/files/rs-4310954/v1/5474449ad93f9ea213ce9f06.png"},{"id":55634881,"identity":"ec5d3fc7-543a-4ed8-a46d-ab8368f4c967","added_by":"auto","created_at":"2024-04-30 20:18:51","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":285615,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociation between p53 and response to NET.\u003c/strong\u003e \u003cstrong\u003e(A) \u003c/strong\u003ePercentage (%) of p53 positive (+) cells in matched pre- and post-NET samples. The lines matching the pre- and post-treatment samples are colour-coded to indicate the magnitude of the change: Blue lines indicate a decrease (n=19), black lines indicate a decrease to zero (n=23), red lines indicate an increase (n=7) and green lines indicate no change (n=2). \u003cstrong\u003e(B-C)\u003c/strong\u003e Association of p53 + cells (%) pre-NET with percentage (%) of Ki67 positive (+) cells at surgery (B) and mPEPI score (C). \u003cstrong\u003e(D-E)\u003c/strong\u003e Association of p53 + cells (%) post-NET with Ki67 + cells (%) at surgery (D) and mPEPI score (E). \u003cstrong\u003e(F-G)\u003c/strong\u003e Association of the changes in p53 (∆p53) with mPEPI score (F) and tumour cellularity size (TCS, mm) (G). Spearman correlation coefficients (rho) and p values are shown for correlation analyses (B and D). Mann-Whitney test p values are indicated when two groups comparison was calculated.\u003c/p\u003e","description":"","filename":"BCRLopezetalMainFigures4.png","url":"https://assets-eu.researchsquare.com/files/rs-4310954/v1/4c49e4d427ca8fd42a97e003.png"},{"id":55634497,"identity":"5e0626dd-8a09-4e73-818f-41aecb39f449","added_by":"auto","created_at":"2024-04-30 20:10:51","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":352730,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eChanges in\u003c/strong\u003e \u003cstrong\u003eHER2 status after NET and their association with response to therapy.\u003c/strong\u003e \u003cstrong\u003e(A) \u003c/strong\u003eAlluvial diagram showing the changes in HER2 status observed in pre- and post-NET matched samples. \u003cstrong\u003e(B) \u003c/strong\u003ePie charts representing the HER2 status distribution in pre-NET (upper panel) and post-NET (lower panel) samples.\u003c/p\u003e","description":"","filename":"BCRLopezetalMainFigures5.png","url":"https://assets-eu.researchsquare.com/files/rs-4310954/v1/7bb05d6832969d60e0001a4d.png"},{"id":56904611,"identity":"6d495139-353a-483d-8eff-d046716a80b6","added_by":"auto","created_at":"2024-05-22 03:09:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2727053,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4310954/v1/29eadb44-2b29-4d4e-8ef3-eefb28ff27ff.pdf"},{"id":55634499,"identity":"d0891ac4-3bc6-48b1-b8f0-3763e6e45f06","added_by":"auto","created_at":"2024-04-30 20:10:51","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":770204,"visible":true,"origin":"","legend":"","description":"","filename":"LopezVelazcosuppmaterial.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4310954/v1/8f87e2c1617a9e740e5e59a8.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Molecular characterization of the residual disease after neoadjuvant endocrine therapy in ER+/HER2- breast cancer uncovers biomarkers of tumour response","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eBreast cancer (BC) is a complex and heterogeneous disease representing the most common malignancy among women worldwide\u0026nbsp;[1]. BC comprises different clinical, histopathological and molecular subtypes. Those tumours expressing oestrogen receptor \u0026alpha; (ER+) and not overexpressing human epidermal growth factor receptor-2 (HER2-) (hereafter referred to as ER+/HER2- BC) constitute the most common subtype, accounting for 65\u0026ndash;70% of all breast tumours\u0026nbsp;[2]. In early and advanced-stage disease, the cornerstone of its systemic treatment is endocrine therapy (ET)\u0026nbsp;[3], usually administered, in the early setting, as adjuvant therapy (after surgery) for 5\u0026ndash;10 years\u0026nbsp;[4]. Although the use of neoadjuvant (before surgery) ET (NET) in ER+/HER2- BC disease is not fully implemented yet in the clinical practice, recent studies and clinical guidelines recommend NET in postmenopausal patients with low-risk ER+/HER2- tumours\u0026nbsp;[5\u0026ndash;9]\u0026nbsp; as it is as effective as neoadjuvant chemotherapy (NACT), but with lower toxicity\u0026nbsp;[10\u0026ndash;13]. Similarly to other neoadjuvant therapies, NET also allows an \u003cem\u003ein vivo\u003c/em\u003e evaluation of treatment sensitivity by monitoring of tumour response and offers the opportunity of interrupting inefficient or toxic therapies\u0026nbsp;[14]. Moreover, post-NET clinical management (e.g. surgery planning or adjuvant therapy) can be based on data obtained during this treatment, allowing personalised strategies\u0026nbsp;[15,16]. Despite all this, potential barriers condition the application of this therapeutic strategy in the clinical practice, as recently highlighted by experts in the field\u0026nbsp;[10,17,18]. One of them is the lack of robust and reproducible biomarkers to assess response and long-term prognosis after NET. In 2012, the Food and Drug Administration (FDA) indicated that the complete pathologic response (pCR) could be used as a surrogate endpoint for neoadjuvant therapy\u0026nbsp;[19]. However, it has been well-documented that pCR is not a suitable endpoint in the case of NET, mainly due to the low rate of pCRs (\u0026lt;5%) observed in this context\u0026nbsp;[20\u0026ndash;25]. Ki67 quantification\u0026nbsp;[26,27]\u0026nbsp;and the preoperative endocrine prognostic index (PEPI) score\u0026nbsp;[28]\u0026nbsp;are the main biomarkers currently available to evaluate prognosis after NET. Early changes in Ki67, while on NET treatment, have both a prognostic and predictive value\u0026nbsp;[26]. However, Ki67 evaluation is not a reproducible parameter across laboratories (e.g. The International Ki67 in Breast Cancer Working Group (IKWG) and others do not recommend their use to guide routine clinical care\u0026nbsp;[27,29,30]). Moreover, mPEPI and Ki67 are not independent parameters as PEPI calculation takes into account Ki67 levels, among others\u0026nbsp;[28]). Nevertheless, NET is the perfect scenario for biomarkers research, as it allows the parallel study of diagnostic biopsies (pre-treatment) and their corresponding post-treatment surgical specimens, increasing its popularity as a highly relevant translational platform\u0026nbsp;[17,31]. Indeed, some potential prognostic and/or predictive biomarkers and endpoints have been intensely debated in the last years\u0026nbsp;[10,25,31\u0026ndash;38]. In this context, our group has previously analysed tumour dynamics after NET in ER+/HER2- BC patients, describing that imaging techniques underestimate tumour size after NET and proposing a new biomarker of tumour response, called tumour cellularity size (TCS), that takes into account that NET response generates a diffuse-cell loss pattern\u0026nbsp;[25]. This TCS lacks further prognostic validation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHere we searched for clinically relevant molecular reporters of NET response in a multicentre population of ER+/HER2- BC patients by using different molecular approaches: PAM50 based intrinsic subtype gene expression panel, protein evaluation of well-established key proteins involved in tumorigenesis and HER2-Low status evaluation. \u0026nbsp;\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eStudy population\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis is a multicentric study with clinical data from 131 postmenopausal women with histologically confirmed, untreated, invasive, operable, non-metastatic ER+/HER2- BC treated with NET prior to surgery between 2005 and 2019. Data were prospectively collected and retrospectively analysed. Clinical characteristics of patients were similar independently of their hospital of origin. Except contraindicated, an aromatase inhibitor was the therapeutic option chosen for NET. Informed consent was obtained from all patients. The study was conducted in accordance with the Declaration of Helsinki and Good Clinical Practice guidelines as well as authorised by the Spain Health Authority and the local Ethics Committee. For the analyses, 3 representative and overlapping subcohorts were defined, as explained below. \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eGene expression data and subtyping according to PAM50 signature\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe gene expression-based PAM50\u0026nbsp;analysis was performed in matching diagnostic biopsies and surgical specimens (subcohort #1, n=58). For RNA purification, 10 \u0026mu;m formalin-fixed paraffin-embedded (FFPE) slides with a minimum of 100 ng of total RNA were used to measure the expression of the PAM50 panel through the nCounter platform (Nanostring Technologies, Seattle, WA, USA). Hematoxylin and eosin-stained slides from\u0026nbsp;core biopsies and surgical specimens were examined to confirm the presence of tumour cells in the areas selected for this analysis. We employed the\u0026nbsp;nSolver 4.0 Software to analyse gene expression, which was expressed as log-base 2 and normalised using 5 housekeeping genes (\u003cem\u003eACTB\u003c/em\u003e, \u003cem\u003eMRPL19\u003c/em\u003e, \u003cem\u003ePSMC4\u003c/em\u003e, \u003cem\u003eRPLP0\u003c/em\u003e, and \u003cem\u003eSF3A1\u003c/em\u003e). As previously described\u0026nbsp;[40\u0026ndash;43], Prosigna\u0026reg;-PAM50 predictor database was used to determine the intrinsic subtype of each sample. They were classified as: luminal A (LumA), luminal B (LumB), HER2-enriched (HER2-E), basal-like (basal) or normal-like (normal) intrinsic BC subtypes. This platform also allowed us to calculate the risk of recurrence (ROR) indexes: ROR-S (associated to the intrinsic subtypes gene set) and ROR-P (ROR index plus a proliferation-related index). Both scores classified patients as low-, intermediate- or high-risk, using the following pre-defined cut-off values (respectively, for ROR-S: \u0026lt;24; 24\u0026ndash;53; \u0026gt;53; and for ROR-P: \u0026lt;12; 12\u0026ndash;53; \u0026gt;53)\u0026nbsp;[41]. We calculated the change in ROR-S (\u0026Delta;ROR-S) after NET as: \u0026Delta;ROR-S = [(\u003cem\u003eROR-S\u003c/em\u003e in surgery specimen) \u0026ndash; (\u003cem\u003eROR-S\u003c/em\u003e in baseline biopsy)] / |ROR-S in baseline biopsy|. According to this, an increase in ROR after NET implicates a positive value of \u0026Delta;ROR-S and a decrease a negative value in \u0026Delta;ROR-S. Moreover, the absolute value of \u0026Delta;ROR-S indicates the change magnitude.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eHistopathological analyses\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor all patients, we obtain FFPE diagnostic core biopsies and post- treatment surgical (after NET) specimens. The surgical specimens were examined by a pathologist to determine pathological tumour size (PTS; corresponding to the major diameter of the tumour in millimetres). In all samples (n=131), we determined the expression of oestrogen receptor (ER), progesterone receptor (PR) and Ki67 using international standards\u0026nbsp;[44,45]. When possible, residual tumour cellularity (%) was evaluated and tumour cellularity size (TCS) calculated, as previously described\u0026nbsp;[25]. Briefly, TCS is calculated as the product of the PTS multiplied by the percentage of epithelial cellularity and is expressed in millimetres.\u003c/p\u003e\n\u003cp\u003eModified PEPI (mPEPI) score was determined according to the characteristics of the surgical specimen,\u0026nbsp;as previously published\u0026nbsp;[28,46]\u0026nbsp;(n=120). It includes PTS value, Ki67 levels and nodal status, and patients are classified into 3 risk groups with an associated differential risk of relapse.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eImmunohistochemistry (IHC) analyses of Bcl2, Sox2, p53, p21 and p16 protein levels were performed in core FFPE diagnostic biopsies and a tissue microarray (TMA) of surgical specimens (subcohort #2, n=61) by using the Ventana Roche system. Staining values for these biomarkers were expressed as i) the percentage of positive tumour nuclei, and ii) the change (∆\u003cem\u003ebiomarker\u003c/em\u003e) after NET was calculated as: ∆\u003cem\u003ebiomarker\u003c/em\u003e = [(\u003cem\u003ebiomarker\u003c/em\u003e (%) in surgery specimen) \u0026ndash; (\u003cem\u003ebiomarker\u003c/em\u003e (%) in baseline biopsy)] / (\u003cem\u003ebiomarker\u003c/em\u003e (%) in baseline biopsy).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe also evaluated HER2 expression by IHC (Ventana System Roche) and following the most recent guidelines from American Society of Clinical Oncology/College of American Pathologists (ASCO/CAP) for HER2 testing\u0026nbsp;[47\u0026ndash;49]. We evaluated HER2 status in baseline biopsies and surgery specimens (subcohort #3, n=99). We complied with the terminology proposed by Denkert et al. in which tumours were considered as: 1) HER2-positive (HER2+) in case of IHC score 3+ and/or \u003cem\u003eERBB2\u003c/em\u003e gene (encoding HER2) amplification by \u003cem\u003ein situ\u003c/em\u003e hybridisation (ISH); 2) HER2-0 in case of IHC score zero; and 3) HER2-low in case of IHC scores 1+ or 2+, in the absence of gene amplification by ISH\u0026nbsp;[36]; being HER2-0 and HER2-low tumours encompassed as HER2-negative (HER2-).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eStatistical analyses\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGraphPad Prism version 9\u0026nbsp;was used to carry out statistical analyses.\u0026nbsp;For the descriptive statistical analyses, minimum, maximum, median and mean values were calculated for continuous variables. Wilcoxon matched-pairs, Mann Whitney and Kruskal-Wallis tests were performed to compare variables with non-Gaussian distributions. Chi-square or Fisher\u0026acute;s tests were used to determine differences between expected frequencies. Spearman\u0026acute;s r coefficients (rho) were used to evaluate correlations (with a 95% of confidence interval). \u003cem\u003ep\u003c/em\u003e values \u0026lt; 0.05 were considered statistically significant. Unless otherwise specified, histograms represent mean values +/- standard error of the mean (SEM) and individual values are also included to visualise data dispersion.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCohort description and patients\u0026rsquo; distribution into subcohorts\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn total, data and samples from 131 patients with early ER+/HER2- BC were analysed.\u0026nbsp;The main characteristics of the patients and tumours,\u0026nbsp;their surgical management and the pathological changes after NET are summarised in \u003cstrong\u003eTable 1\u003c/strong\u003e.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eThe study population presented a mean age at diagnosis of 70 (47-93) and the mean NET duration before surgery was 9 months (2-40). Letrozole was the endocrine treatment administered in most cases\u0026nbsp;(94.7%)\u0026nbsp;and breast-conserving surgery was performed in 79% of the patients.\u0026nbsp;Of note, one patient was diagnosed with bilateral disease, and her two tumours were independently considered in our analyses. Moreover, two patients achieved a pCR after NET (1.5% of patients) and, consequently, their post-NET surgical samples were not available for biological evaluation. Apart from these mentioned exceptions, matched samples from each patient were analysed (a core diagnostic biopsy or pre-NET sample, and the surgical specimen or post-NET sample). \u003cstrong\u003eSuppl Mat Fig S1\u003c/strong\u003e shows the distribution of patients in 3 subcohorts and the different analyses performed in each of them. The distribution of patients into each subcohort took into account the sample availability (e.g. location) and sample quality required for each analysis. Our subcohorts are representative of the general cohort as observed in\u0026nbsp;\u003cstrong\u003eSuppl Mat Tab 1\u003c/strong\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003ePAM50 analysis (intrinsic subtype and ROR score) in core biopsies can predict NET response\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe 50 gene expression-based PAM50 assay allows the classification of BC into 5 intrinsic biological subtypes and generates risk of recurrence (ROR) scores for each sample\u0026nbsp;[39]. However, the utility of PAM50 to predict treatment benefit requires further investigation in the specific context of NET.\u0026nbsp;In this regard, we selected a representative subcohort of ER+/HER2- BC\u0026nbsp;patients treated with NET (subcohort #1 in \u003cstrong\u003eSuppl Mat Fig S1\u003c/strong\u003e) and we obtained the PAM50 data (intrinsic subtype and ROR scores) on their diagnostic biopsies (pre-NET) and surgery specimens (post-NET) (\u003cstrong\u003eTable 2\u003c/strong\u003e). As expected, due to the characteristics of our cohort, low number of patients presented HER2-E or basal subtypes. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFirst, to check if our subcohort behaved similarly to others of this kind, we validated previous findings regarding the predictive value of Luminal A (LumA) and Luminal B (LumB) baseline PAM50 categorization in ER+ BC treated with NET\u0026nbsp;[33]. As expected, our results showed that LumA presented lower Ki67 after treatment, \u0026Delta;Ki67 and mPEPI score than LumB, indicating a better biological response to NET and better prognosis (\u003cstrong\u003eFigure 1A-B, Suppl Mat Fig\u0026nbsp;S2A\u003c/strong\u003e).\u0026nbsp; Moreover, to add further evidence of the predictive value of PAM50 evaluation at baseline (pre-NET), we analysed ROR score data. We observed that tumours with high ROR scores had worse response to NET evaluated as higher Ki67 % at surgery and a lower change in Ki67 (measured by \u0026Delta;Ki67) together with a worse prognosis with a higher mPEPI score (\u003cstrong\u003eFigure 1C-D,\u0026nbsp;Suppl Mat Fig\u0026nbsp;S2B and Table 2\u003c/strong\u003e). Moreover, tumour cellularity size (TCS), a novel biomarker for NET response described for the first time by Lopez-Velazco et al.\u0026nbsp;[25]\u0026nbsp;correlates with ROR-S at baseline (pre-NET) (\u003cstrong\u003eFigure 1E\u003c/strong\u003e). No differences were found for ROR-S and ROR-P (\u003cstrong\u003eSuppl Mat Fig S2C-F\u003c/strong\u003e). These results support that PAM50 analysis in the diagnostic biopsy could help to personalize the use of NET, conjointly with other clinical parameters.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eConversion from Luminal A to Normal-like intrinsic subtype after NET is associated with better response\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs mentioned above, NET is a therapeutic strategy that allows the characterisation of tumour response by comparing diagnostic biopsies (pre-NET) and surgical specimens (post-NET)\u0026nbsp;[17,31]. Nowadays, limited data exist regarding intrinsic subtype changes in paired pre- and post- NET samples of ER+/HER2- BC patients. That is why we characterised the changes in the intrinsic subtype upon NET in matched samples and analysed their relationship to treatment response.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe distribution of PAM50-based intrinsic subtypes among pre- and post-NET samples and the value of the main NET biomarkers of each change group are described in \u003cstrong\u003eTable 2, Figure 2 and Suppl Mat Fig S3A-C\u003c/strong\u003e.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eOur results showed that the two most represented profiles of intrinsic subtype after NET were: the group of tumours with a persistent LumA status (36% of our patients) and the group of tumours with a conversion of LumA to Normal-like status upon therapy (26% of patients). We wondered whether there could be a difference in response/prognosis to NET between these two groups which share the LumA pre-NET status. Our results showed that tumours changing from LumA to Normal-like after NET presented: lower Ki67 levels at surgery, a significant decrease in Ki67 (\u0026Delta;Ki67), lower mPEPI score and lower TCS (\u003cstrong\u003eFigure 2C-E, Suppl Mat Fig\u0026nbsp;S3D\u003c/strong\u003e). Importantly, Lopez-Velazco et al.\u0026nbsp;[25]\u0026nbsp;proposed a cutoff for TCS at 2.5mm. We found that 92% of Persistent LumA tumours presented a high TCS (\u0026gt;2.5mm), while LumA to normal subgroup was enriched in low TCS (\u0026le; 2.5 mm) tumours (\u003cstrong\u003eFigure 2F\u003c/strong\u003e).\u0026nbsp;Later, we assessed whether these results could be conditioned by the tumour size or epithelial cellularity content. Our analyses showed that our two populations of interest did not statistically differ in these parameters\u0026nbsp;although a tendency is observed regarding to cellularity\u0026nbsp;(\u003cstrong\u003eFigure 2G-H\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNext, we analysed if the change in ROR may also help us to evaluate NET response and prognosis. We observed that, in our cohort, ROR values are significantly lower after NET compared to the ones obtained pre-NET (\u003cstrong\u003eTable 2, Figure 3A, Suppl Mat Fig S3E\u003c/strong\u003e). Interestingly, \u003cstrong\u003eFigure 3A\u003c/strong\u003e shows that none of tumours changing from Luminal A (LumA) to normal-like (normal) intrinsic subtype increases its ROR after NET. The potential of ROR change (∆ROR) between pre- and post-NET samples as a marker of NET response and prognosis has not been largely explored in previous studies. Our results indicate that ∆ROR value should be taken into consideration, at least in the case of ROR-S. First, we plotted ∆ROR-S values from all patients (\u003cstrong\u003eFigure 3B\u003c/strong\u003e) and found 3 subpopulations: i) ∆ROR-S \u0026ge; 0 (including patients with no change or increase of ROR-S after NET; 14 out of 59); ii) 0\u0026gt;\u0026Delta;ROR-S\u0026gt;-2.5 (including patients with a slight decrease in ROR after NET; 31 out of 59); and iii) \u0026Delta;ROR-S\u0026le;-2.5 (including patients with an accentuated change in ROR after NET; 14 out of 59). \u0026Delta;ROR-S values are significantly different between ii) and iii) according to statistical analyses, indicating that a cut-off at \u0026Delta;ROR-S = -2.5 could stratify two subpopulations of patients. At this point, we wondered whether these two subpopulations might show differences in their NET response and prognosis. Statistically significant differences in Ki67, \u0026Delta;Ki67 and mPEPI between these novel subgroups of patients subdivided taking into account their \u0026Delta;ROR-S values (\u003cstrong\u003eFigure 3C-E\u003c/strong\u003e). \u0026nbsp;Interestingly, subgroups ii) and iii) present clear differences, being subgroup ii) more similar to i) than to iii). TCS results showed a trend to lower TCS in the iii) group, although no statistically significant was reached (\u003cstrong\u003eFigure 4F\u003c/strong\u003e) likely due to insufficient number of patients per group. Thus, this novel stratification of patients into 3 subgroups according to their changes in ROR-S (\u0026Delta;ROR-S, establishing cut-off at \u0026Delta;ROR-S=0 and \u0026Delta;ROR-S=-2.5) has revealed relevant differences in NET response and prognosis between patients depending on the magnitude of their ROR decrease after NET.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn summary, our data indicate that analysing the change in the intrinsic subtype or ROR value upon NET can provide information about response to NET and patient prognosis that could help in the clinical practice to, for example, determine adjuvant therapy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eQuantification of p53 levels before and after NET may contribute to asess NET response\u003c/em\u003e\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePrevious studies indicate that biological markers (e.g. senescence or apoptosis markers) may be useful to determine the efficacy of a neoadjuvant therapy (mainly, chemotherapy) in BC patients\u0026nbsp;[50\u0026ndash;53]. However, few data are published in the NET setting in ER+/HER2- BC. Here, we study the protein levels of different biomarkers of apoptosis (Bcl2), cancer stem cell phenotype (Sox2) and senescence (p53, p21 and p16) by IHC in matched pre- and post-NET samples as well as the value of their change after NET. For that, we selected a novel subcohort (subcohort #2 in \u003cstrong\u003eSuppl Mat Fig S1\u003c/strong\u003e), representative of our global cohort of ER+/HER2- BC\u0026nbsp;patients treated with NET, composed of 61 patients. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe quantifications of the mentioned markers in each sample type are shown in \u003cstrong\u003eFigure 4A\u0026nbsp;\u003c/strong\u003eand\u003cstrong\u003e\u0026nbsp;Suppl Mat Fig\u0026nbsp;S4A-D\u003c/strong\u003e. The percentage of positive cells for p53 and p21, two senescence markers, showed a clear decrease after NET (FC\u0026ge;-2), while the percentage of positive cells for Bcl2 (apoptosis marker) presented also a significant decrease after NET but the magnitude change was lower (FC=-1.1). To determine the predictive/prognostic value of these markers, we compared them (pre-NET, post-NET or changes) with the validated predictive NET biomarkers (Ki67 expression at surgery, ∆Ki67 and with the prognostic mPEPI score). These analyses were performed with all the markers, although Sox2, Bcl2 and p16 did not show any association (data not shown). First, our results showed that, in the pre-NET samples, only p53 might have predictive capacity since a higher expression was associated with higher Ki67 and mPEPI grading, indicating poor response and prognosis after NET (\u003cstrong\u003eFigure 4B-C\u003c/strong\u003e). Next, we studied the protein levels of the mentioned markers in our post-NET samples. We observed that the expression of p53 and p21 was inversely associated with response to NET (\u003cstrong\u003eFigure 4D-E and Suppl Mat Fig 5A-B\u003c/strong\u003e). Finally, we decided to study if the change in the mentioned markers before and after NET may be related to\u0026nbsp;NET response. Importantly, we found that the change in levels of p53 (∆p53) after NET may be associated to response/prognosis due to their correlation with mPEPI score and TCS (cutoff at 2.5mm) (\u003cstrong\u003eFigure 5F-G\u003c/strong\u003e). The change in levels of p21 (∆p21) after NET may be also associated to NET response according to its correlation with Ki67 (\u003cstrong\u003eSuppl Mat Fig\u0026nbsp;S5C\u003c/strong\u003e). However, further validation is needed to establish their value as long-term prognostic biomarkers\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn summary, in the search for new biomarkers of response with long-term prognostic value upon NET, our results suggest that, among another biological biomarkers evaluated, the percentage of positive cells for p53 (assessed in pre-NET and post-NET samples and its change) showed promising results in terms of its ability to characterise residual cell populations with prognostic interest, deserving this further validation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eA change in HER2 status is observed after NET, although it is not associated to tumour response\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHER2-low status has gained huge attention during the last years in the field due to the fact that some studies point out that it should be considered as an independent biologic subtype distinct from HER2-0 BC, although its clinic implications are not well-elucidated\u0026nbsp;[49,54]. Here, we explored the relationship of HER2-low status with NET response and prognosis. For that,\u0026nbsp;we defined a novel subcohort (subcohort #3 in \u003cstrong\u003eSuppl Mat Fig S1\u003c/strong\u003e), representative of our global cohort of ER+/HER2- BC\u0026nbsp;patients treated with NET, composed of 99 patients. We collected retrospectively the patients\u0026rsquo; data related to HER2 in their diagnostic biopsies (pre-NET) and surgical specimens (post-NET) (\u003cstrong\u003eTable 3 and Figure 5A-B\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFirst, we studied if subdividing pre-NET samples into HER2-0 and HER2-low may predict NET response. We observed that, at diagnosis, pathologists\u0026rsquo; analyses indicated that most of our patients could be classified as HER2-low (HER2-low: n=66; HER2-0: n=33), but we did not find any relationship between HER2 status and NET predictive/prognostic markers (Ki67, mPEPI and ∆Ki67; \u003cstrong\u003eSuppl Mat Fig 6A-C\u003c/strong\u003e). Similarly, when we analysed if the stratification of post-NET samples into HER2-0 and HER2-low groups may be related to Ki67 levels and PEPI score, we did not obtain any positive result (HER2-low: n=32; HER2-0: n=62) (\u003cstrong\u003eSuppl Mat Fig 6D-F\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eImportantly, by comparing HER2 status between pre- and post-NET samples (\u003cstrong\u003eFigure 5A-B\u003c/strong\u003e), we observed a statistically significant decrease in HER2 scoring after NET and, consequently, enrichment in HER2-0 samples (\u003cstrong\u003eSuppl Mat Fig 7A\u003c/strong\u003e). \u0026nbsp;Around half of the samples presented a discordant pre- vs post-NET HER2 status (40 of 94 patients (43%) when comparing HER2-0 vs HER2-low; \u003cstrong\u003eTable 3\u003c/strong\u003e). In detail, 36 patients (38%) changed their HER2 status from HER2-low (pre-NET) to HER2-0 (post NET) (\u003cstrong\u003eSuppl Mat Fig 7A and Table 3\u003c/strong\u003e), being this the most prevalent change observed in our cohort. Thus, we checked whether this change may be used as a marker of response to NET (compared to HER2-0 and HER2-low persistent patients), but we did not observe any relationship with the aforementioned NET prognostic/predictive markers (\u003cstrong\u003eSuppl Mat Fig 7B-D\u003c/strong\u003e). We explored other analyses, comparing different groups, but we did not obtain any positive relationship between HER2 status change after NET and NET response (data not shown).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTaken together, the results of our ER+/HER2- cohort indicate that, after NET, a generalized decrease in HER2 status can be observed but it is not associated to NET response. Analysing pre-NET and post-NET data individually neither supports the interpretation of HER2-low status as a distinct biologic entity in the context of NET response in ER+/HER2- patients.\u0026nbsp;\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eSuccessful treatment of early breast cancer relies, nowadays, on an optimal combination of a minimally invasive local therapy (i.e. surgery and radiotherapy) and a personalized systemic therapy that maximizes reduction of risk of relapse and minimizes toxicity\u0026nbsp;[31,55]. Until recently this optimization of systemic therapy was merely based in basal risk of relapse being more toxic therapies (e.g. chemotherapy or prolonged endocrine therapy) justified when risk of relapse was higher as anatomically defined by node involvement or larger tumor size, for example\u0026nbsp;[56]. Current development of new systemic therapies in the adjuvant setting beyond chemotherapy or anti-estrogens has boosted the need for an individualized approach that, overcoming mere risk-based sub-population definition, includes sensitivity to the different therapies as a cornerstone of patient-based decision. Out of the three major subtypes of BC, namely ER+/HER2-, HER2 amplified and triple negative, only patients with the two latter have, available, an individual-sensitivity approach. This way, patients with HER2 amplified tumors will be routinely exposed to chemotherapy plus a double anti-HER2 antibody treatment prior to therapy and, then, depending if pCR is achieved or not, exposed to either antibodies or a combination of antibodies with chemotherapy in the post-surgical therapy. Similarly, the post-surgical therapy of triple negative patients will vastly depend on the achievement of a complete response after primary systemic therapy pre-surgery\u0026nbsp;[7,57].\u003c/p\u003e\n\u003cp\u003eAlthough the above-mentioned advances in personalization of therapies have been made possible in the least populated subgroups of BC patients, those with ER+/HER2- tumors, the vast majority, lack this possibility. This unmet need is becoming particularly urgent when new therapies (e.g. CDK 4/6 inhibitors) are being introduced in the adjuvant setting of these patients without a real guide for individual sensitivity selection of patients and still relying in mostly anatomical risk factors\u0026nbsp;[58]. The most relevant breakthrough in treatment de-escalation in luminal patients has come through the introduction of the polygenic platforms that assess the risk of relapse in a more biology-based way but are still vastly depending in risk of relapse assessment\u0026nbsp;[59,60]. Although it is well known that ER+/HER2- BC patients are, as a group, those benefiting less from toxic adjuvant chemotherapy, to date, no consensus has been reached on how to robustly evaluate the benefit from the cornerstone systemic therapy on these patients, namely endocrine therapy, in the pre-operative setting so the decision on post-surgical systemic therapy (e.g. chemotherapy, CDK 4/6 inhibitors, prolonged and/or combined antiestrogen therapy) is facilitated\u0026nbsp;[61,62]. And this is mainly due to the scarcity of pCR following NET.\u003c/p\u003e\n\u003cp\u003eFour major considerations should be beard in mind when searching for reproducible, valid markers of response assessment to NET. First, any method of assessment needs to be developed following biomarkers standard procedures, including, in particular in this setting, validation in its ability to predict long-term outcomes. Second, currently available biomarkers in this setting, vastly depend on Ki67 which is, as discussed, far from being a reproducible biomarker\u0026nbsp;[29,30,63,64]. Third, multiple gene-expression platforms have demonstrated high utility and reproducibility in ER+/HER2- BC both for molecular subtype and prognosis assessment\u0026nbsp;[32,60]. And fourth, pathologic response has a particular scattered pattern in these tumours, that usually does not include complete response, but with marked tumor cell population reduction\u0026nbsp;[22,25,65\u0026ndash;67].\u0026nbsp;Hence it looks logical to seek for robust, reproducible, NET sensitivity reporters, among already available multigene expression panels, on the one side, and to produce a robust system of pathologic assessment of response beyond those developed for primary chemotherapy.\u0026nbsp;A disadvantage of most studies covering this scientific question is that they are usually focused on one biomarker and they do not combine different analyses in the same cohort. \u0026nbsp;This makes it difficult to comparatively analyse the different proposed biomarkers.\u003c/p\u003e\n\u003cp\u003eIn this and our previous work we propose, coinciding with other groups, two markers that could be considered strong candidates for further validation as reporters of optimal/suboptimal response to NET, namely, the change into a \u003cem\u003eNormal-like\u003c/em\u003e subtype, and the Tumor Cellularity Score\u0026nbsp;[25,68,69].\u003c/p\u003e\n\u003cp\u003eAs a clear neat trait of our series, that might be considered a limitation, we have developed the study in a series of small tumours with good favorable features. Our cohort characteristics are similar to others treated with NET\u0026nbsp;[21,23,26,70]. As in our, those cohorts were composed by patients with small tumours (\u0026lt;2cm evaluated by USS) with low rate of positive lymph node at diagnosis (confirmed by node biopsy) and with a low rate of pCR (\u0026lt;5%) after therapy. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePrevious evidence about PAM50 subtypes in baseline and residual tumours following NACT plus Trastuzumab in HER2 positive BC showed that after therapy, the two principal changes in PAM50-based intrinsic subtype were an increase in Luminal A and Normal-like subtypes\u0026nbsp;[71]. In the NET context, Gil Gil and collaborators\u0026nbsp;[72]\u0026nbsp;analysed PAM50 profile in surgical specimens from patients of a retrospective study of 119 postmenopausal women with HR-positive stage II-III BC treated with NET. \u0026nbsp;The PAM50 subtype distribution after NET was Luminal A 54.3%, Normal-like 24.3%; HER2-enriched 16,5%, Luminal B 1% and basal 1%. These results are in line with our study in which the two more frequent PAM50 subtypes after NET were Luminal A and Normal-like in 28 (48%) and 19 (33%) patients, respectively. In a substantially similar study to ours Schettini et al\u0026nbsp;[68]\u0026nbsp;have reported a conversion rate to Normal-like 8% pre to 42% post NET. This group, additionally, provide the first evidence on the good long-term prognostic significance that this conversion may have. Although a lower percentage of tumor cells in the post-NET sample may contribute to the normal expression profile, it cannot be the only factor since, statistically, our samples pre- and post-NET, do not differ in epithelial cellularity. This change to Normal-like subtype has a clear additional value to the diagnostic phenotype were this subtype is rare. This is particularly relevant bearing in mind that those tumours with a Luminal A subtype after primary therapy still beard a considerable risk of relapse as shown in a study on patients treated with NACT \u0026nbsp;by Denkert et al\u0026nbsp;[73], who reported how patients with a Lum A profile at surgery still beard a substantial risk of relapse with an invasive Disease Free Survival rate of 79,5% at 3 years. This way we hypothesized that, beyond the initial better prognosis of Luminal A patients at diagnosis, a conversion to Normal-like after NET may characterize a population of extremely good prognosis that could be considered for de-escalation trials.\u003c/p\u003e\n\u003cp\u003eWe have shown that those tumours changing from Luminal A to Normal-like after NET presented lower Ki67-stained cells at surgery, a significant decrease in Ki67 (\u0026Delta;Ki67) and lower mPEPI score. Additionally, we found, using our recently described TCS as a mean to comprehensively capture the prognostic value of the tumor size and the effect of NET on cellularity, that tumors converting to Normal-like had a neat lower TCS than those remaining Luminal A.\u003c/p\u003e\n\u003cp\u003eMoreover, current studies are describing the changes in gene expression assays \u0026nbsp;(such as Oncotype or PAM50) observed after NST\u0026nbsp;[74]. Such is the case of Pascual et al\u0026nbsp;[75]\u0026nbsp;who described that in the CORALLEEN trial (N=106, Luminal B early BC treated with NST), the PAM50 proliferation score significantly decreased after Ribociclib plus Letrozole and after NACT between baseline and time of surgery for samples with a complete cell cycle arrest (CCCA, KI67\u0026lt;2.7%) and non-CCCA samples (p\u0026lt;0.001).\u003c/p\u003e\n\u003cp\u003eAcknowledging the clear limitations of the use of ROR scores in post-NET samples where there is no evidence at all of their prognostic value, we hypothesized that the ROR-score ROR-S, which is less sensitivity to proliferation abolition than ROR-P and more dependent on subtype discrimination, might help to characterize in a dynamic way the prognostic implications of the change in tumour characteristics after NET. We demonstrate that the\u0026nbsp;\u0026Delta;ROR-S has a clear correlation with the currently available reporters of response and good prognosis after NET (\u0026Delta;Ki67 and mPEPI score). What is more, virtually no patient with a tumor converting from Luminal A to Normal-like lacked a decreased in\u0026nbsp;\u0026Delta;ROR-S which is, in our view, an extremely encouraging feature of the change in subtype as a landmark of extremely good prognosis characterization. Ueno et al. \u0026nbsp;and Hilal et al. published recently\u0026nbsp;[76,77]\u0026nbsp;an initially contradictory result to ours with regard to ROR post-NET performance in terms of prognostic prediction. To put that into context it should be recognized that, firstly, these authors used OncotypeDx\u0026reg; based ROR, a different technology much more likely to be influenced by the proliferation abolition after NET than ROR-S score, which is enriched in subtype specific genes. Additionally, we calculated\u0026nbsp;\u0026Delta;ROR normalizing by basal levels, which differs from this other study where this normalization was not taken into account.\u003c/p\u003e\n\u003cp\u003eIn a much more exploratory aspect of our study, we analysed the relationship between NET and apoptosis and senescence-associated markers. We investigated as to whether the lack of pCR, characteristic of NET, which can be attributed at least partly to a reduction of apoptosis paralleling the reduction in proliferation, can be captured by the change in markers of apoptosis and, so, used to characterize patterns of response. Out of our investigation we found that p53 levels (pre- and post-NET) and its change after therapy (decrease) are clearly related to NET. Interestingly, these results are in line with those observed by Mueller et al,\u0026nbsp;[35]. In their study p53 IHC staining decrease after NET and they described a relationship between this change with response to therapy in terms of Ki67 index. Also, as ours, their results indicated that pre- and post p53 levels per se, are related to response to NET and that those patients with poorer response presented higher levels of p53 in both evaluations.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Approximately 80% of BC not overexpressing HER2 are currently defined as HER2-negative\u0026nbsp;[49]. However, half of these BC show some degree of HER2 expression by IHC and are currently defined as HER2-Low. Several studies, boosted by the current availability of HER2-Low targeted therapies\u0026nbsp;[54,78,79]\u0026nbsp;have shown conflicting results and, currently, it is highly unclear whether HER2-Low BC should be considered an individual biologic subtype distinct from HER2-0 BC\u0026nbsp;[54]. The biology and predictive-prognostic implications of a HER2-Low BC are not yet well-elucidated and some studies in the neoadjuvant setting (mainly in chemotherapy) in ER+/HER2- BC have shown contradictory results when comparing pCR rates between HER2-Low and HER2-0 status (in pre-treatment diagnostic biopsy)\u0026nbsp;[36,78,80\u0026ndash;82]. In this context, our results do not endorse a differential behavior of HER-2 low versus HER-2 0 tumors but highlight the need to re-test HER2 after NET on the basis of the significative incidence of HER2 expression loss. As a shortcoming of our results to this extent we highlight that our HER2 results were assessed before the current awareness of pathologists on the importance of the differentiation between HER2 low and HER2 0 in the HER2 negative population.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAs a final conclusion we considered that the present results, along with those of other groups, uncover the intrinsic subtype change after NET, in particular the conversion to Normal-like, as a putative biomarker characterizing a population that benefit highly from endocrine therapy. If considered conjointly with the pathologic evaluation of response via reproducible methodology as might be the TCS assessment, we could easily characterize that population benefiting most from endocrine approach and bearing a potential very good prognosis. These two biomarkers should be validated in independent series including long-term outcomes. If confirmed in their value, the population they characterize should be considered optimal for de-escalation trials allowing NET to become a real tool for treatment personalization in ER+/HER2 negative early breast cancer.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eADDITIONAL INFORMATION\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSupplementary information is available at Breast Cancer Research journal website.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eACKNOWLEDGEMENTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are grateful to the members of our laboratory for critical discussion of this work, and, for their technical assistance, to the Pathology Services of: i) OSI Donostialdea \u0026ndash; Onkologikoa (San Sebasti\u0026aacute;n, Spain), ii) Hospital Cl\u0026iacute;nico Universitario de Valencia (Valencia, Spain), iii) Valencia Oncology Institute (Valencia, Spain), iv) Catalan Institute of Oncology (Badalona, Spain), v) Hospital October 12 (Madrid, Spain), and vi) Hospital Clinic \u0026ndash; Barcelona \u0026ndash; IDIBAPS (Barcelona, Spain).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eETHICS APPROVAL\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted in accordance with the Declaration of Helsinki and Good Clinical Practice guidelines as well as authorised by the Spain Health Authority and the local Ethics Committee.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAUTHOR CONTRIBUTIONS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJILV, SM, AU and MMC analysed the data and wrote the manuscript. AU designed the study. AU and MMC co-supervised the study. KE performed the histopathological analyses. JILV, SM, MO, AL, LA, IE, MH, EB, JG, VQ, MF, SA, IA and AU collected and examined patient data. LP and AP performed PAM50 assay. All authors reviewed the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFUNDING\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was funded by Instituto de Salud Carlos III (ISCIII) grants: PI21/01208, PI20/01253, CP18/00076 and FI19/00193 co-funded by the European Union, Basque Department of Health (2020111040), Fundaci\u0026oacute;n SEOM (SEOM Avon Fellowship 2020) and Ikerbasque Basque Research Foundation. The group also received funds from the breast cancer patient\u0026rsquo;s charity Katxalin and from Roche Farma S.A. JILV is funded by an AECC PhD Fellowship (PRDGI19007LOPE) and SMF is funded by Juan de la Cierva Fellowship of Spanish Agency for Research (AEI).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDATA AVAILABILITY STATEMENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data for this study are available upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eFerlay J, Colombet M, Soerjomataram I, Parkin DM, Pi\u0026ntilde;eros M, Znaor A, et al. Cancer statistics for the year 2020: An overview. Int J Cancer. 2021;149:778\u0026ndash;89. \u003c/li\u003e\n\u003cli\u003eHowlader N, Altekruse SF, Li CI, Chen VW, Clarke CA, Ries LAG, et al. US incidence of breast cancer subtypes defined by joint hormone receptor and HER2 status. J Natl Cancer Inst. 2014;106. \u003c/li\u003e\n\u003cli\u003eBurstein HJ. Systemic Therapy for Estrogen Receptor\u0026ndash;Positive, HER2-Negative Breast Cancer. 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PLoS One. 2012;7:9\u0026ndash;11. \u003c/li\u003e\n\u003cli\u003eBae SY, Lee JH, Bae JW, Jung SP. Differences in prognosis by p53 expression after neoadjuvant chemotherapy in triple-negative breast cancer. Ann Surg Treat Res. 2020;98:291\u0026ndash;8. \u003c/li\u003e\n\u003cli\u003eAnelli A, Brentani RR, Gadelha AP, de Albuquerque AA, Soares F. Correlation of p53 status with outcome of neoadjuvant chemotherapy using paclitaxel and doxorubicin in stage IIIB breast cancer. Ann Oncol [Internet]. 2003;14:428\u0026ndash;32. Available from: https://doi.org/10.1093/annonc/mdg104\u003c/li\u003e\n\u003cli\u003eAbdel-Fatah TMA, Perry C, Dickinson P, Ball G, Moseley P, Madhusudan S, et al. Bcl2 is an independent prognostic marker of triple negative breast cancer (TNBC) and predicts response to anthracycline combination (ATC) chemotherapy (CT) in adjuvant and neoadjuvant settings. Ann Oncol [Internet]. 2013;24:2801\u0026ndash;7. Available from: https://doi.org/10.1093/annonc/mdt277\u003c/li\u003e\n\u003cli\u003eTarantino P, Hamilton E, Tolaney SM, Cortes J, Morganti S, Ferraro E, et al. HER2-Low breast cancer: Pathological and clinical landscape. J Clin Oncol. 2020;38:1951\u0026ndash;62. \u003c/li\u003e\n\u003cli\u003eHarbeck N, Penault-Llorca F, Cortes J, Gnant M, Houssami N, Poortmans P, et al. Breast cancer. Nat. Rev. Dis. Prim. 2019. \u003c/li\u003e\n\u003cli\u003eRashmi Kumar N, Schonfeld R, Gradishar WJ, Lurie RH, Moran MS, Abraham J, et al. NCCN Guidelines Version 1.2024 Breast Cancer [Internet]. 2024. Available from: https://www.nccn.\u003c/li\u003e\n\u003cli\u003eLoibl S, Andr\u0026eacute; F, Bachelot T, Barrios CH, Bergh J, Burstein HJ, et al. Early breast cancer: ESMO Clinical Practice Guideline for diagnosis, treatment and follow-up. Ann Oncol [Internet]. 2024;35:159\u0026ndash;82. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0923753423051049\u003c/li\u003e\n\u003cli\u003eSlamon D, Lipatov O, Nowecki Z, McAndrew N, Kukielka-Budny B, Stroyakovskiy D, et al. Ribociclib plus Endocrine Therapy in Early Breast Cancer. N Engl J Med. 2024;390:1080\u0026ndash;91. \u003c/li\u003e\n\u003cli\u003ePrat A, Ellis MJ, Perou CM. Practical implications of gene-expression-based assays for breast oncologists. Nat. Rev. Clin. Oncol. 2012. p. 48\u0026ndash;57. \u003c/li\u003e\n\u003cli\u003eKwa M, Makris A, Esteva FJ. Clinical utility of gene-expression signatures in early stage breast cancer. Nat. Rev. Clin. Oncol. Nature Publishing Group; 2017. p. 595\u0026ndash;610. \u003c/li\u003e\n\u003cli\u003eMa CX, Suman VJ, Sanati S, Vij K, Anurag M, Leitch AM, et al. Endocrine-Sensitive Disease Rate in Postmenopausal Patients With Estrogen Receptor\u0026ndash;Rich/ERBB2-Negative Breast Cancer Receiving Neoadjuvant Anastrozole, Fulvestrant, or Their Combination. JAMA Oncol [Internet]. 2024;10:362. Available from: https://jamanetwork.com/journals/jamaoncology/fullarticle/2813940\u003c/li\u003e\n\u003cli\u003ePrat A, Saura C, Pascual T, Hernando C, Mu\u0026ntilde;oz M, Par\u0026eacute; L, et al. Ribociclib plus letrozole versus chemotherapy for postmenopausal women with hormone receptor-positive, HER2-negative, luminal B breast cancer (CORALLEEN): an open-label, multicentre, randomised, phase 2 trial. Lancet Oncol [Internet]. 2020;21:33\u0026ndash;43. Available from: https://linkinghub.elsevier.com/retrieve/pii/S1470204519307867\u003c/li\u003e\n\u003cli\u003eAcs B, Leung SCY, Kidwell KM, Arun I, Augulis R, Badve SS, et al. Systematically higher Ki67 scores on core biopsy samples compared to corresponding resection specimen in breast cancer: a multi-operator and multi-institutional study. Mod Pathol. 2022;35:1362\u0026ndash;9. \u003c/li\u003e\n\u003cli\u003eSmith IE, Walsh G, Skene A, Llombart A, Mayordomo JI, Detre S, et al. A phase II placebo-controlled trial of neoadjuvant anastrozole alone or with gefitinib in early breast cancer. J Clin Oncol. 2007;25:3816\u0026ndash;22. \u003c/li\u003e\n\u003cli\u003eHeil J, Kuerer HM, Pfob A, Rauch G, Sinn HP, Golatta M, et al. Eliminating the breast cancer surgery paradigm after neoadjuvant systemic therapy: current evidence and future challenges. Ann. Oncol. Elsevier Ltd; 2020. p. 61\u0026ndash;71. \u003c/li\u003e\n\u003cli\u003ePastorello RG, Laws A, Grossmith S, King C, McGrath M, Mittendorf EA, et al. Clinico-pathologic predictors of patterns of residual disease following neoadjuvant chemotherapy for breast cancer. Mod Pathol. 2021;34:875\u0026ndash;82. \u003c/li\u003e\n\u003cli\u003eLaws A, Pastorello R, Dey T, Grossmith S, King C, McGrath M, et al. Impact of the Histologic Pattern of Residual Tumor After Neoadjuvant Chemotherapy on Recurrence and Survival in Stage I\u0026ndash;III Breast Cancer. Ann Surg Oncol [Internet]. 2022;29:7726\u0026ndash;36. Available from: https://doi.org/10.1245/s10434-022-12054-6\u003c/li\u003e\n\u003cli\u003eSchettini F, Bras\u0026oacute;-Maristany F, Bergamino M, Rivera P, Bravo RG, Bel\u0026eacute;n Rodr\u0026iacute;guez A, et al. Gene expression before and after neoadjuvant chemotherapy or endocrine therapy and survival outcomes in hormone receptor-positive, HER2-negative breast cancer: the NEOENDO study. Available from: https://doi.org/10.21203/rs.3.rs-3937385/v1\u003c/li\u003e\n\u003cli\u003eDunbier AK, Anderson H, Ghazoui Z, Salter J, Parker JS, Perou CM, et al. Association between breast cancer subtypes and response to neoadjuvant anastrozole. Steroids. 2011. p. 736\u0026ndash;40. \u003c/li\u003e\n\u003cli\u003eSkriver SK, Jensen MB, Knoop AS, Ejlertsen B, Laenkholm AV. Tumour-infiltrating lymphocytes and response to neoadjuvant letrozole in patients with early oestrogen receptor-positive breast cancer: Analysis from a nationwide phase II DBCG trial. Breast Cancer Res. 2020;22. \u003c/li\u003e\n\u003cli\u003ePernas S, Petit A, Climent F, Par\u0026eacute; L, Perez-Martin J, Ventura L, et al. PAM50 Subtypes in Baseline and Residual Tumors Following Neoadjuvant Trastuzumab-Based Chemotherapy in HER2-Positive Breast Cancer: A Consecutive-Series From a Single Institution. Front Oncol. 2019;9. \u003c/li\u003e\n\u003cli\u003eGil Gil MJ, Perez FJ, Soler Monso T, Pascual T, Galv\u0026aacute;n P, Pare L, et al. Prognostic value of PAM50 in residual breast cancer following neoadjuvant endocrine therapy (NET): A retrospective analysis with long follow-up. J Clin Oncol [Internet]. 2019;37:575\u0026ndash;575. Available from: http://ascopubs.org/doi/10.1200/JCO.2019.37.15_suppl.575\u003c/li\u003e\n\u003cli\u003eDenkert C, Marm\u0026eacute; F, Martin M, Untch M, Bonnefoi HR, Witkiewicz AK, et al. Subgroup of post-neoadjuvant luminal-B tumors assessed by HTG in PENELOPE-B investigating palbociclib in high risk HER2-/HR+ breast cancer with residual disease. J Clin Oncol [Internet]. 2021;39:519\u0026ndash;519. Available from: https://ascopubs.org/doi/10.1200/JCO.2021.39.15_suppl.519\u003c/li\u003e\n\u003cli\u003eUeno T, Saji S, Masuda N, Iwata H, Kuroi K, Sato N, et al. Changes in Recurrence Score by neoadjuvant endocrine therapy of breast cancer and their prognostic implication. ESMO Open [Internet]. 2019;4:e000476. Available from: https://linkinghub.elsevier.com/retrieve/pii/S2059702920301915\u003c/li\u003e\n\u003cli\u003ePascual T, Fernandez-Martinez A, Agrawal Y, Pfefferle AD, Chic N, Bras\u0026oacute;-Maristany F, et al. Cell-cycle inhibition and immune microenvironment in breast cancer treated with ribociclib and letrozole or chemotherapy. npj Breast Cancer. 2024;10. \u003c/li\u003e\n\u003cli\u003eUeno T, Saji S, Masuda N, Kuroi K, Sato N, Takei H, et al. Impact of clinical response to neoadjuvant endocrine therapy on patient outcomes: a follow-up study of JFMC34-0601 multicentre prospective neoadjuvant endocrine trial. ESMO Open [Internet]. 2018;3:e000314. Available from: https://linkinghub.elsevier.com/retrieve/pii/S2059702920323437\u003c/li\u003e\n\u003cli\u003eHilal T, Mi L, Ertz‐Archambault NM, Almquist DR, Anderson KS, Gray RJ, et al. Change in 21‐gene Recurrence Score result after exposure to neo‐adjuvant endocrine therapy in patients with operable breast cancer. Breast J [Internet]. 2020;26:1449\u0026ndash;51. Available from: https://onlinelibrary.wiley.com/doi/10.1111/tbj.13731\u003c/li\u003e\n\u003cli\u003eZhou S, Liu T, Kuang X, Zhen T, Shi H, Lin Y, et al. Comparison of clinicopathological characteristics and response to neoadjuvant chemotherapy between HER2-low and HER2-zero breast cancer. Breast [Internet]. 2023;67:1\u0026ndash;7. Available from: https://doi.org/10.1016/j.breast.2022.12.006\u003c/li\u003e\n\u003cli\u003eZhang H, Katerji H, Turner BM, Hicks DG. HER2-Low Breast Cancers. Am. J. Clin. Pathol. Oxford University Press; 2022. p. 328\u0026ndash;36. \u003c/li\u003e\n\u003cli\u003eMiglietta F, Griguolo G, Bottosso M, Giarratano T, Lo Mele M, Fassan M, et al. HER2-low-positive breast cancer: evolution from primary tumor to residual disease after neoadjuvant treatment. npj Breast Cancer. 2022;8:1\u0026ndash;7. \u003c/li\u003e\n\u003cli\u003eKang S, Lee SH, Lee HJ, Jeong H, Jeong JH, Kim JE, et al. Pathological complete response, long-term outcomes, and recurrence patterns in HER2-low versus HER2-zero breast cancer after neoadjuvant chemotherapy. Eur J Cancer [Internet]. 2022;176:30\u0026ndash;40. Available from: https://doi.org/10.1016/j.ejca.2022.08.031\u003c/li\u003e\n\u003cli\u003eSchettini F, Chic N, Bras\u0026oacute;-Maristany F, Par\u0026eacute; L, Pascual T, Conte B, et al. Clinical, pathological, and PAM50 gene expression features of HER2-low breast cancer. npj Breast Cancer [Internet]. 2021;7. Available from: http://dx.doi.org/10.1038/s41523-020-00208-2\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1. Tumour characteristics.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.711340206185568%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.98969072164948%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePre-NET diagnostic biopsies (n=132*)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.98969072164948%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePost-NET surgical resections (n=132\u0026ordf;)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eGrade [n (%)]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.711340206185568%\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.98969072164948%\"\u003e\n \u003cp\u003e27 (20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.98969072164948%\"\u003e\n \u003cp\u003e39 (29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" rowspan=\"4\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.436781609195403%\"\u003e\n \u003cp\u003eII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.7816091954023%\"\u003e\n \u003cp\u003e99 (75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.7816091954023%\"\u003e\n \u003cp\u003e89 (67)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.436781609195403%\"\u003e\n \u003cp\u003eIII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.7816091954023%\"\u003e\n \u003cp\u003e6 (5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.7816091954023%\"\u003e\n \u003cp\u003e2 (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.436781609195403%\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.7816091954023%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.7816091954023%\"\u003e\n \u003cp\u003e2 (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eHistology [n (%)]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.711340206185568%\"\u003e\n \u003cp\u003eNST\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.98969072164948%\"\u003e\n \u003cp\u003e110 (83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.98969072164948%\"\u003e\n \u003cp\u003e104 (79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" rowspan=\"4\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.436781609195403%\"\u003e\n \u003cp\u003eILC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.7816091954023%\"\u003e\n \u003cp\u003e12 (9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.7816091954023%\"\u003e\n \u003cp\u003e15 (11)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.436781609195403%\"\u003e\n \u003cp\u003eOther special type\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.7816091954023%\"\u003e\n \u003cp\u003e10 (8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.7816091954023%\"\u003e\n \u003cp\u003e11 (8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.436781609195403%\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.7816091954023%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.7816091954023%\"\u003e\n \u003cp\u003e2 (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eT state [n (%)]\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.711340206185568%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.98969072164948%\"\u003e\n \u003cp\u003e\u003cstrong\u003ecT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.98969072164948%\"\u003e\n \u003cp\u003e\u003cstrong\u003eypT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.711340206185568%\"\u003e\n \u003cp\u003eT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.98969072164948%\"\u003e\n \u003cp\u003e52 (39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.98969072164948%\"\u003e\n \u003cp\u003e80 (61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.711340206185568%\"\u003e\n \u003cp\u003eT2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.98969072164948%\"\u003e\n \u003cp\u003e69 (52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.98969072164948%\"\u003e\n \u003cp\u003e46 (35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.711340206185568%\"\u003e\n \u003cp\u003eT3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.98969072164948%\"\u003e\n \u003cp\u003e10 (8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.98969072164948%\"\u003e\n \u003cp\u003e3 (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.711340206185568%\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.98969072164948%\"\u003e\n \u003cp\u003e1 (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.98969072164948%\"\u003e\n \u003cp\u003e3 (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eN state [n (%)]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.711340206185568%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.98969072164948%\"\u003e\n \u003cp\u003e\u003cstrong\u003ecN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.98969072164948%\"\u003e\n \u003cp\u003e\u003cstrong\u003eypN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.711340206185568%\"\u003e\n \u003cp\u003eNegative\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.98969072164948%\"\u003e\n \u003cp\u003e109 (82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.98969072164948%\"\u003e\n \u003cp\u003e83 (63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.711340206185568%\"\u003e\n \u003cp\u003ePositive\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.98969072164948%\"\u003e\n \u003cp\u003e22 (17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.98969072164948%\"\u003e\n \u003cp\u003e40 (30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.711340206185568%\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.98969072164948%\"\u003e\n \u003cp\u003e1 (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.98969072164948%\"\u003e\n \u003cp\u003e9 (7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003ePositive cells (%) [mean (range)]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.711340206185568%\"\u003e\n \u003cp\u003eKi67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.98969072164948%\"\u003e\n \u003cp\u003e21 (1-80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.98969072164948%\"\u003e\n \u003cp\u003e10 (1-80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.711340206185568%\"\u003e\n \u003cp\u003eOestrogen receptor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.98969072164948%\"\u003e\n \u003cp\u003e93 (8-100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.98969072164948%\"\u003e\n \u003cp\u003e90 (0-100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.711340206185568%\"\u003e\n \u003cp\u003eProgesterone receptor\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.98969072164948%\"\u003e\n \u003cp\u003e62 (0-100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.98969072164948%\"\u003e\n \u003cp\u003e17 (0-99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*One patient was diagnosed with bilateral disease, and her two tumours were independently considered. \u0026nbsp;\u0026ordf;Two tumours were not evaluable for biological characteristics at surgery because the patient achieved a pCR. c/yp tumour (T) status was determined clinically (by RMN or USS) and pathologically before and after NET.c/yp axillary node status (N) was determined clinically (by USS) and pathologically before and after NET N/A: not available, NST: no special type, ILC: invasive lobular carcinoma, ns: non-significant. Chi-square or Wilcoxon matched-pairs signed rank tests performed. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e \u003cstrong\u003ePAM50-intrinsic subtype profiles and ROR scores in pre and post NET samples.\u003c/strong\u003e % Ki67 expression at surgery, ∆KI67 and mPEPI score groups are included in the table. ROR-S and P scores are shown as L: low, M:medium or H:High.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.26530612244898%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eIntrinsic subtype\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003en (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eROR-P score (pre-NET)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eROR-S score (pre-NET)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eKi67 + cells (%) at surgery\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\"\u003e\n \u003cp\u003e\u003cstrong\u003e∆Ki67\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003emPEPI score groups \u0026nbsp;(n)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.818181818181818%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePre-NET\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.954545454545454%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePost-NET\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.681818181818182%\"\u003e\n \u003cp\u003e\u003cstrong\u003eL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.681818181818182%\"\u003e\n \u003cp\u003e\u003cstrong\u003eM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.681818181818182%\"\u003e\n \u003cp\u003e\u003cstrong\u003eH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.545454545454546%\"\u003e\n \u003cp\u003e\u003cstrong\u003eL\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.681818181818182%\"\u003e\n \u003cp\u003e\u003cstrong\u003eM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.681818181818182%\"\u003e\n \u003cp\u003e\u003cstrong\u003eH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.636363636363637%\"\u003e\n \u003cp\u003e\u003cstrong\u003e% mean (range)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.454545454545453%\"\u003e\n \u003cp\u003e\u003cstrong\u003emean (range)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.545454545454546%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.818181818181818%\"\u003e\n \u003cp\u003e\u003cstrong\u003e1-3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.818181818181818%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026gt;3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003eLumA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.368421052631579%\"\u003e\n \u003cp\u003eLumA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.368421052631579%\"\u003e\n \u003cp\u003e21 (36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.2631578947368425%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.2631578947368425%\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.2631578947368425%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.2105263157894735%\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.2631578947368425%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.2631578947368425%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003e8 (1 - 27)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.94736842105263%\"\u003e\n \u003cp\u003e-0,41 (-0,95 to 1)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.2105263157894735%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003eLumA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.368421052631579%\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.368421052631579%\"\u003e\n \u003cp\u003e15 (26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.2631578947368425%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.2631578947368425%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.2631578947368425%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.2105263157894735%\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.2631578947368425%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.2631578947368425%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003e2 (0 - 20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.94736842105263%\"\u003e\n \u003cp\u003e-0,83 (-0,05 to -1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.2105263157894735%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003eLumA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.368421052631579%\"\u003e\n \u003cp\u003eLumB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.368421052631579%\"\u003e\n \u003cp\u003e3 (5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.2631578947368425%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.2631578947368425%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.2631578947368425%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.2105263157894735%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.2631578947368425%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.2631578947368425%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003e12 (1-18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.94736842105263%\"\u003e\n \u003cp\u003e-0,56 (-0,6 to -0,97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.2105263157894735%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003eLumA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.368421052631579%\"\u003e\n \u003cp\u003eHER2-E\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.368421052631579%\"\u003e\n \u003cp\u003e1 (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.2631578947368425%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.2631578947368425%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.2631578947368425%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.2105263157894735%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.2631578947368425%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.2631578947368425%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.94736842105263%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.2105263157894735%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003eLumB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.368421052631579%\"\u003e\n \u003cp\u003eLumA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.368421052631579%\"\u003e\n \u003cp\u003e6 (10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.2631578947368425%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.2631578947368425%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.2631578947368425%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.2105263157894735%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.2631578947368425%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.2631578947368425%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003e23 (5 - 50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.94736842105263%\"\u003e\n \u003cp\u003e0,12 (-0,64 to 1,25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.2105263157894735%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003eLumB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.368421052631579%\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.368421052631579%\"\u003e\n \u003cp\u003e3 (5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.2631578947368425%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.2631578947368425%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.2631578947368425%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.2105263157894735%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.2631578947368425%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.2631578947368425%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003e8 (6 - 10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.94736842105263%\"\u003e\n \u003cp\u003e-0,6 (-0,6 to -0,7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.2105263157894735%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003eLumB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.368421052631579%\"\u003e\n \u003cp\u003eLumB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.368421052631579%\"\u003e\n \u003cp\u003e5 (9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.2631578947368425%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.2631578947368425%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.2631578947368425%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.2105263157894735%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.2631578947368425%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.2631578947368425%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003e38 (22 - 45)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.94736842105263%\"\u003e\n \u003cp\u003e-0,23 (0 to -0,44)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.2105263157894735%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003eHER2-E\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.368421052631579%\"\u003e\n \u003cp\u003eHER2-E\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.368421052631579%\"\u003e\n \u003cp\u003e2 (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.2631578947368425%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.2631578947368425%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.2631578947368425%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.2105263157894735%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.2631578947368425%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.2631578947368425%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003e34 (28 - 39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.94736842105263%\"\u003e\n \u003cp\u003e0,23 (0,16 to 0,30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.2105263157894735%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003eHER2-E\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.368421052631579%\"\u003e\n \u003cp\u003eLumA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.368421052631579%\"\u003e\n \u003cp\u003e1 (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.2631578947368425%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.2631578947368425%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.2631578947368425%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.2105263157894735%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.2631578947368425%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.2631578947368425%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.94736842105263%\"\u003e\n \u003cp\u003e0,22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.2105263157894735%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.368421052631579%\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.368421052631579%\"\u003e\n \u003cp\u003e1 (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.2631578947368425%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.2631578947368425%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.2631578947368425%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.2105263157894735%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.2631578947368425%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.2631578947368425%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.94736842105263%\"\u003e\n \u003cp\u003e-0,9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.2105263157894735%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.68421052631579%\" colspan=\"2\"\u003e\n \u003cp\u003eTotal [n (%)]\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.368421052631579%\"\u003e\n \u003cp\u003e58 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.2631578947368425%\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.2631578947368425%\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.2631578947368425%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.2105263157894735%\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.2631578947368425%\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.2631578947368425%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.94736842105263%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.2105263157894735%\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*Not all values were available for all patients.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.\u003c/strong\u003e \u003cstrong\u003eRetrospective analysis of HER2 status in our ER+/HER2- cohort of patients treated with NET.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"8\"\u003e\n \u003cp\u003e\u003cstrong\u003ePre- and post-NET HER2 status.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.927835051546392%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003ePre-NET\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" style=\"width: 13.0254%;\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" style=\"width: 12.7344%;\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.927835051546392%\" colspan=\"2\" style=\"width: 23.1953%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePost-NET\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\" style=\"width: 5.3555%;\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" style=\"width: 8.1445%;\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.927835051546392%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eHER2-0\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" style=\"width: 13.0254%;\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" style=\"width: 12.7344%;\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.927835051546392%\" colspan=\"2\" valign=\"bottom\" style=\"width: 23.1953%;\"\u003e\n \u003cp\u003eHER2-0\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\" style=\"width: 5.3555%;\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" style=\"width: 8.1445%;\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.927835051546392%\" colspan=\"2\"\u003e\n \u003cp\u003eHER2-low\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" style=\"width: 13.0254%;\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" style=\"width: 12.7344%;\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.927835051546392%\" colspan=\"2\" style=\"width: 23.1953%;\"\u003e\n \u003cp\u003eHER2-low\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\" style=\"width: 5.3555%;\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" style=\"width: 8.1445%;\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.927835051546392%\" colspan=\"2\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" style=\"width: 13.0254%;\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" style=\"width: 12.7344%;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.927835051546392%\" colspan=\"2\" style=\"width: 23.1953%;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\" style=\"width: 5.3555%;\"\u003e\n \u003cp\u003e94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" style=\"width: 8.1445%;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.711340206185568%\" rowspan=\"3\"\u003e\n \u003cp\u003eHER2-low\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\" valign=\"bottom\"\u003e\n \u003cp\u003e+1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" style=\"width: 13.0254%;\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" style=\"width: 12.7344%;\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.711340206185568%\" rowspan=\"3\" style=\"width: 13.3203%;\"\u003e\n \u003cp\u003eHER2-low\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\" style=\"width: 9.9297%;\"\u003e\n \u003cp\u003e+1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\" style=\"width: 5.3555%;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" style=\"width: 8.1445%;\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.72549019607843%\" valign=\"bottom\"\u003e\n \u003cp\u003e+2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.607843137254903%\" style=\"width: 13.0254%;\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.607843137254903%\" style=\"width: 12.7344%;\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.72549019607843%\" style=\"width: 9.9297%;\"\u003e\n \u003cp\u003e+2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.72549019607843%\" style=\"width: 5.3555%;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.607843137254903%\" style=\"width: 8.1445%;\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.72549019607843%\" valign=\"bottom\"\u003e\n \u003cp\u003e+3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.607843137254903%\" style=\"width: 13.0254%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.607843137254903%\" style=\"width: 12.7344%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.72549019607843%\" style=\"width: 9.9297%;\"\u003e\n \u003cp\u003e+3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.72549019607843%\" style=\"width: 5.3555%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.607843137254903%\" style=\"width: 8.1445%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.927835051546392%\" colspan=\"2\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" style=\"width: 13.0254%;\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" style=\"width: 12.7344%;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.927835051546392%\" colspan=\"2\" style=\"width: 23.1953%;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\" style=\"width: 5.3555%;\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" style=\"width: 8.1445%;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eHER2, human epidermal growth factor receptor 2.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Neoadjuvant endocrine therapy, aromatase inhibitors, PAM50, normal-like subtype, HER2-low, p53, preoperative endocrine prognostic index (PEPI) score, Ki67","lastPublishedDoi":"10.21203/rs.3.rs-4310954/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4310954/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground \u003c/strong\u003eNeoadjuvant endocrine therapy (NET) in oestrogen receptor-positive (ER+) /HER2-negative (HER2-) breast cancer (BC) allows an in vivo evaluation of treatment sensitivity by the monitoring of tumour response and offers the opportunity of personalized BC therapy. However, the lack of reproducible biomarkers to assess response and long-term prognosis after NET, beyond Ki67 levels and preoperative endocrine prognostic index score (mPEPI), is a significant barrier to increase its indications.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods \u003c/strong\u003eIn this study we searched clinically relevant molecular reporters of NET response and prognosis in a multicentre population of ER+/HER2- BC patients by using: PAM50 gene expression panel, protein evaluation of key proteins involved in tumorigenesis and HER2-Low status evaluation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults \u003c/strong\u003eOn a cohort of 131 patients our results show that PAM50 categorization (Luminal A vs Luminal B and ROR high vs ROR Low) predicts response to NET, with Luminal A and low ROR score tumours showing better response and prognosis than Luminal B and high ROR score. Moreover, tumours changing from Luminal A to Normal-like after NET presented a significant larger decrease in Ki67 levels at surgery, lower mPEPI score and a lower tumour cellularity size than those with persistent Luminal A status. In addition, we identify that the percentage of p53 positive cells in pre- and post-NET samples are associated with response or prognosis to NET.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion \u003c/strong\u003eOur findings highlight the change of intrinsic subtype to normal-like after NET as a putative biomarker characterizing a population that benefit highly from NET. If considered conjointly with the pathologic evaluation of response, we could characterize a tumour population benefiting most from endocrine approach and bearing a potential very good prognosis.\u003c/p\u003e","manuscriptTitle":"Molecular characterization of the residual disease after neoadjuvant endocrine therapy in ER+/HER2- breast cancer uncovers biomarkers of tumour response","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-30 20:10:46","doi":"10.21203/rs.3.rs-4310954/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9903c292-92ad-440d-9a5e-375cd69e103d","owner":[],"postedDate":"April 30th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-05-22T03:08:55+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-30 20:10:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4310954","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4310954","identity":"rs-4310954","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}