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```of 10, were associated with this parameter, pointing to a negative outcome of premenopausal breast cancer for high expression of FOXP3 [25]. High percentages of standard tumor-infiltrating lymphocytes (TILs) were associated with highly proliferative, high-grade triple-negative or HER2 positive breast cancers and correlated with complete pathological response and improved disease-free survival (DFS) [26]. Based on results from many large randomized trials, high baseline TILs predict higher rates of pathologic complete response (pCR) in breast cancer patients receiving neoadjuvant chemotherapy, regardless of the subtype, and predict a disease-free survival benefit in triple-negative breast cancer (TNBC) and HER2+ breast cancer. On the other hand, in ER+/HER2- breast cancer, low-to-moderate TIL expression has been associated with more favorable prognosis compared with high TILs, suggesting potential biological differences between distinct immunogenic tumor microenvironments [27,28]. Furthermore, PD-L1 is an immune biomarker of response to checkpoint inhibition in breast cancer. Indeed, the FDA recently approved the anti-PD-L1 antibody atezolizumab in combination with nab-paclitaxel for the treatment of patients with unresectable, locally advanced or metastatic TNBC whose tumors express PD-L1 (on tumor-infiltrating immune cells of any intensity covering $\ge 1\%$ of the tumor area) [29,30]. However, little is known about the response to anti-PD-1 therapy (pembrolizumab) plus chemotherapy in patients with early TNBC. It has been reported that the percentage of patients with a pathological complete response was significantly higher among those who received pembrolizumab plus chemotherapy than among those who received placebo plus chemotherapy [31]. Overall, understanding the role of immune infiltrates and target molecules can help select patients for adjuvant therapy or clinical trials with immunotherapeutics. In contrast with the wide knowledge of microenvironment modifications of primary invasive breast cancers, less information is available about molecular and microenvironment features of recurrences. In this study, we assessed the molecular features of pairs of primary and recurrent breast cancers in order to identify potential biomarkers associated with recurrence. Our analysis included standard histopathological parameters and expression of target molecules. Moreover, the expression profile of a large panel of genes involved in tumor biology and microenvironment was evaluated in selected pairs of primary and recurrent tumors. ## 2. Patients and Methods ### 2.1. Selected Cases A retrospective cohort of 30 women, with a primary invasive breast cancer diagnosis and a history of local or regional recurrence between 2004 and 2018, was selected. For all patients, tissue blocks of both primary tumor and recurrence were available at the Pathology Unit of the University of Bari. Clinico-pathological data, including age at diagnosis, surgery type, therapy, date of recurrence, and date of last follow-up or death, were retrieved from clinical charts. The study was approved by the Ethics Committee of the University of Bari Medical School (no. 5937/2018). ### 2.2. Immunohistochemical Analysis Tissue microarrays (TMAs) of primary tumors and corresponding recurrences were prepared using a manual tissue arrayer (MTA-1, Beecher Instruments Inc., Sun Prairie, WI, USA). For each case, three representative tumor areas of 1 mm in diameter were selected from hematoxylin and eosin (H&E)-stained sections and sampled from the donor blocks. Standard 4-$\mu$m sections were obtained from TMAs and used for immunohistochemical (IHC) staining on a Dako Autostainer Link 48 (Dako, Glostrup, Denmark). The markers, antibodies, and technical specifications are detailed in Table S1. Primary breast cancers and corresponding recurrences were immunostained for Estrogen Receptor (ER), Progesterone Receptor (PgR), HER2, Ki67, androgen receptor (AR), mismatch repair proteins (MMR: MLH1, MSH2, MSH6, and PMS2), mismatch repair protein-1 (MRE11), tumor-associated antigen (p53), Programmed Death-Ligand 1 (PD-L1), and FOXP3. Quantitative assessment of immunohistochemical markers was performed by two pathologists (S.S. and R.D.) independently. For ER and PgR, a cutoff value of $\ge 1\%$ positively stained tumor cell nuclei was used to define receptor positivity. HER2 status was evaluated according to the updated 2018 ASCO/CAP guidelines [32]. Slices scored as $2+$ (equivocal) by IHC were subjected to Dual ISH (DISH) analysis using the Ventana HER2 Dual ISH DNA Probe Cocktail Assay (Ventana Medical Systems, Tucson, AZ, USA) on a BenchMark ULTRA platform (Roche-Ventana). Ki67, AR, p53, and MRE11 were scored as percentage of positive cells over total tumor cells, counting at least 500 tumor cells in the hotspot. Ki-67 was categorized as low ($<20\%$) or high ($\ge 20\%$). Tumors with strong nuclear p53 expression in $\ge 80\%$ of tumor cells, or with a complete absence of nuclear p53 staining, were considered to harbor mutated-type p53; those showing heterogeneous p53 expression were categorized as wild-type [33]. MMR status was considered defective (dMMR) if any of the proteins (MLH1, MSH2, MSH6, or PMS2) showed complete loss of nuclear staining in tumor cells with retained staining in internal positive controls (normal epithelial or stromal cells). For AR, a cutoff value of $\ge 10\%$ of positively stained tumor nuclei was adopted [34]. For MRE11, a cutoff of $<21.1\%$ positive tumor cells was associated with homologous recombination deficiency (HRD) and poor prognosis [35,36]. For FOXP3, regulatory T cells (Tregs) were counted in the three representative cores per tumor, and the average number of FOXP3-positive lymphocytes per core was calculated. Finally, PD-L1 positivity was defined as $\ge 1\%$ of tumor-infiltrating immune cells (clone SP142) of any intensity covering the tumor area. ### 2.3. RNA Extraction and Gene Expression Profiling We selected five pairs of primary breast cancers and corresponding recurrences based on their clinical-pathological features (e.g., age of patient, time to recurrence, hormonal status, and recurrence site). For each tumor, total RNA was extracted from three 10-$\mu$m-thick formalin-fixed, paraffin-embedded (FFPE) sections using the RNeasy FFPE Kit (Qiagen, Hilden, Germany), following the manufacturer's instructions. RNA concentration and purity were assessed using a NanoDrop 2000 spectrophotometer (Thermo Scientific, Wilmington, DE, USA). Samples with a concentration of at least 50 ng/$\mu$L were used for downstream analysis. Gene expression profiling was performed using the nCounter Breast Cancer 360 (BC360) Panel (NanoString Technologies, Seattle, WA, USA), which enables the evaluation of 770 genes involved in breast cancer biology, including signaling pathways, immune response, and tumor microenvironment, as well as 14 housekeeping genes. Hybridization was performed at 65 °C for 16-18 h, according to the manufacturer's protocol. The hybridized samples were then loaded onto the nCounter Prep Station for purification and binding, and finally scanned on the nCounter Digital Analyzer. Raw data were processed and normalized using the nSolver Analysis Software (v4.0) and the Advanced Analysis plug-in (v2.0, NanoString Technologies). Genes with counts below the background level (defined as the mean plus two standard deviations of the negative control probes) were excluded from further analysis. Differences in gene expression between primary tumors and recurrences were evaluated using a fold-change (FC) threshold of $\ge 1.5$ (or $\le -1.5$) and a $p$-value $<0.05$. ### 2.4. Statistical Analysis Differences in immunohistochemical marker expression between primary tumors and corresponding recurrences were evaluated using the Wilcoxon signed-rank test for continuous variables and the McNemar's test for categorical variables. Statistical analyses were performed using SPSS software (v22.0, SPSS Inc., Chicago, IL, USA). All statistical tests were two-sided, and a $p$-value $<0.05$ was considered statistically significant.