Tumor relapse as a consequence of chemotherapy resistance is a major clinical challenge in advanced stage breast tumors. To identify processes associated with poor clinical outcome, we took a mass spectrometry‐based proteomic approach and analyzed a breast cancer cohort of 113 formalin‐fixed paraffin‐embedded samples. Proteomic profiling of matched tumors before and after chemotherapy, and tumor‐adjacent normal tissue, all from the same patients, allowed us to define eight patterns of protein level changes, two of which correlate to better chemotherapy response. Supervised analysis identified two proteins of proline biosynthesis pathway, PYCR1 and ALDH18A1, that were significantly associated with resistance to treatment based on pattern dominance. Weighted gene correlation network analysis of post‐treatment samples revealed that these proteins are associated with tumor relapse and affect patient survival. Functional analysis showed that knockdown of PYCR1 reduced invasion and migration capabilities of breast cancer cell lines. PYCR1 knockout significantly reduced tumor burden and increased drug sensitivity of orthotopically injected ER‐positive tumor in vivo, thus emphasizing the role of PYCR1 in resistance to chemotherapy.

Purpose: Molecular evolution of tumors during progression, therapy, and metastasis is a major clinical challenge and the main reason for resistance to therapy. We hypothesized that microRNAs (miRNAs) that exhibit similar variation of expression through the course of disease in several patients have a significant function in the tumorigenic process. Experimental design: Exploration of evolving disease by profiling 800 miRNA expression from serial samples of individual breast cancer patients at several time points: pretreatment, posttreatment, lymph nodes, and recurrence sites when available (58 unique samples from 19 patients). Using a dynamic approach for analysis, we identified expression modulation patterns and classified varying miRNAs into one of the eight possible temporal expression patterns. Results: The various patterns were found to be associated with different tumorigenic pathways. The dominant pattern identified an miRNA set that significantly differentiated between disease stages, and its pattern in each patient was also associated with response to therapy. These miRNAs were related to tumor proliferation and to the cell-cycle pathway, and their mRNA targets showed anticorrelated expression. Interestingly, the level of these miRNAs was lowest in matched recurrent samples from distant metastasis, indicating a gradual increase in proliferative potential through the course of disease. Finally, the average expression level of these miRNAs in the pretreatment biopsy was significantly different comparing patients experiencing recurrence to recurrence-free patients. Conclusions: Serial tumor sampling combined with analysis of temporal expression patterns enabled to pinpoint significant signatures characterizing breast cancer progression, associated with response to therapy and with risk of recurrence. Clin Cancer Res; 22(14); 3651–62. ©2016 AACR.

Background: BRCA mutation-associated (BRCAmut) breast cancer represents a heterogeneous group displaying certain molecular features. Claudin-low breast cancers (CLBC) overlap with characteristics of BRCAmut tumors; therefore, we have investigated whether these are identical subtypes. Methods: Using public gene expression data, CLDN, CDH1, 9-cell line claudin-low predictor (9CLCLP) and PAM50 expression was evaluated in BRCAmut and BRCA wild-type (BRCAwt) breast cancer cases focusing on their possible overlap with the CLBC subtype. A separate formalin-fixed, paraffin-embedded (FFPE) cohort of 22 BRCAmut and 19 BRCAwt tumor tissues was used for immunohistochemical examination of AR, CD24, CD44, CK5/6, claudin-1, -3, -4 and -7, E-cadherin, EGFR, estrogen receptor (ER), EZH2, HER2, Ki67, p53, progesterone receptor (PgR) and vimentin expression. Results: In the data sets, CLDN1 (ROC = 0.785, p < 0.001), CDH1 (ROC = 0.785, p < 0.001), CLDN7 (ROC = 0.723, p < 0.001), CLDN3 (ROC = 0.696, p = 0.020) and CLDN4 (ROC = 0.685, p = 0.027) were expressed at higher level in BRCAmut than BRCAwt tumor tissue. The PAM50 subtype differed from the assigned immunohistochemistry (IHC)-based subtype in 30%. Based on accessible 9CLCLP predictor genes, BRCAmut breast cancer does not display the claudin-low phenotype. Utilizing FFPE samples, claudins were evidently expressed in both BRCAmut and BRCAwt cases. However, at the protein level, only claudin-3 expression was higher in BRCAmut tumors, while claudin-1, -4 and -7 and E-cadherin expression was lower compared to BRCAwt cases. A CD24^low/CD44^high phenotype was found in BRCAmut tumors upon comparison with BRCAwt cases (p < 0.001 and p = 0.001, respectively). Conclusions: There is a prominent correlation between the genes under focus herein and BRCA mutation status. BRCAmut tumors bear stem cell characteristics displaying a distinct cell adhesion molecule profile characterized by high expression of CDH1 and CLDN4 according to public gene expression data set analysis, and higher claudin-3 expression as detected by IHC; thus, BRCAmut breast carcinomas are not identical with the previously identified claudin-low subtype of breast cancer.

<div>Abstract<p><b>Purpose:</b> Molecular evolution of tumors during progression, therapy, and metastasis is a major clinical challenge and the main reason for resistance to therapy. We hypothesized that microRNAs (miRNAs) that exhibit similar variation of expression through the course of disease in several patients have a significant function in the tumorigenic process.</p><p><b>Experimental design:</b> Exploration of evolving disease by profiling 800 miRNA expression from serial samples of individual breast cancer patients at several time points: pretreatment, posttreatment, lymph nodes, and recurrence sites when available (58 unique samples from 19 patients). Using a dynamic approach for analysis, we identified expression modulation patterns and classified varying miRNAs into one of the eight possible temporal expression patterns.</p><p><b>Results:</b> The various patterns were found to be associated with different tumorigenic pathways. The dominant pattern identified an miRNA set that significantly differentiated between disease stages, and its pattern in each patient was also associated with response to therapy. These miRNAs were related to tumor proliferation and to the cell-cycle pathway, and their mRNA targets showed anticorrelated expression. Interestingly, the level of these miRNAs was lowest in matched recurrent samples from distant metastasis, indicating a gradual increase in proliferative potential through the course of disease. Finally, the average expression level of these miRNAs in the pretreatment biopsy was significantly different comparing patients experiencing recurrence to recurrence-free patients.</p><p><b>Conclusions:</b> Serial tumor sampling combined with analysis of temporal expression patterns enabled to pinpoint significant signatures characterizing breast cancer progression, associated with response to therapy and with risk of recurrence. <i>Clin Cancer Res; 22(14); 3651–62. ©2016 AACR</i>.</p></div>

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