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Background: Copy number alterations (CNA) are acquired during the evolution of cancers from their early stage to metastatic stage. This study aims at analysing the clinical value of the identified metastasis-associated CNAs both in metastatic breast cancers (mBCs) and early breast cancers (eBCs)., Methods: Single-nucleotide polymorphism (SNP)-array was performed on 926 biopsies from mBC patients, enrolled in SAFIR02-BREAST prospective trial. CNA profiles of eBCs from The Cancer Genome Atlas Breast Invasive Carcinoma (n = 770), Molecular Taxonomy of Breast Cancer International Consortium (n = 1620) and PACS04 trial (n = 243) cohorts were used as references for comparing mBCs and eBCs CNA profiles. Overall survival was the considered survival endpoint., Results: Among the twenty-one genes frequently altered in ER + /HER2- mBCs: focal amplification of TERT was associated with poor outcome in the ER + /HER2- mBC population. Among the ER + /HER2- mBCs patients for whom CDK4/6 inhibitors information before biopsies collection was available: we identified seven genes on post-treatment biopsies, including the cyclin-dependent kinase 4 (CDK4), which was amplified in 9.8% of the ER + /HER2- mBCs pretreated population, as compared to 1.5% in the ER + /HER2- mBCs unpretreated population (P = 2.82E-04) as well as the 3 eBC populations. CDK4 amplification was associated with poor outcome in the ER + /HER2- eBCs., Conclusions: This study provides insights into the biology of mBCs and identifies clinically useful genomic features for future improvement of breast cancer patient management., (© 2024. The Author(s).)

Background: Heterogeneity and lack of targeted therapies represent the two main impediments to precision treatment of triple-negative breast cancer (TNBC), and therefore, molecular subtyping and identification of therapeutic pathways are required to optimize medical care. The aim of the present study was to define robust TNBC subtypes with clinical relevance., Methods: Gene expression profiling by means of DNA chips was conducted in an internal TNBC cohort composed of 238 patients. In addition, external data (n = 257), obtained by using the same DNA chip, were used for validation. Fuzzy clustering was followed by functional annotation of the clusters. Immunohistochemistry was used to confirm transcriptomics results: CD138 and CD20 were used to test for plasma cell and B lymphocyte infiltrations, respectively; MECA79 and CD31 for tertiary lymphoid structures; and UCHL1/PGP9.5 and S100 for neurogenesis., Results: We identified three molecular clusters within TNBC: one molecular apocrine (C1) and two basal-like-enriched (C2 and C3). C2 presented pro-tumorigenic immune response (immune suppressive), high neurogenesis (nerve infiltration), and high biological aggressiveness. In contrast, C3 exhibited adaptive immune response associated with complete B cell differentiation that occurs in tertiary lymphoid structures, and immune checkpoint upregulation. External cohort subtyping by means of the same approach proved the robustness of these results. Furthermore, plasma cell and B lymphocyte infiltrates, tertiary lymphoid structures, and neurogenesis were validated at the protein levels by means of histological evaluation and immunohistochemistry., Conclusion: Our work showed that TNBC can be subcategorized in three different subtypes characterized by marked biological features, some of which could be targeted by specific therapies.

To date, there is no available targeted therapy for patients who are diagnosed with triple-negative breast cancers (TNBC). The aim of this study was to identify a new specific target for specific treatments. Frozen primary tumors were collected from 83 adjuvant therapy-naive TNBC patients. These samples were used for global proteome profiling by iTRAQ-OFFGEL-LC-MS/MS approach in two series: a training cohort (n = 42) and a test set (n = 41). Patients who remains free of local or distant metastasis for a minimum of 5 years after surgery were classified in the no-relapse group; the others were in the relapse group. OPLS and Kaplan-Meier analyses were performed to select candidate markers, which were validated by immunohistochemistry. Three proteins were identified in the training set and validated in the test set by Kaplan-Meier method and immunohistochemistry (IHC): TrpRS as a good prognostic markers and DP and TSP1 as bad prognostic markers. We propose the establishment of an IHC test to calculate the score of TrpRS, DP, and TSP1 in TNBC tumors to evaluate the degree of aggressiveness of the tumors. Finally, we propose that DP and TSP1 could provide therapeutic targets for specific treatments., (© 2015 by The American Society for Biochemistry and Molecular Biology, Inc.)

Background: The standard therapy regimen of conventional osteosarcoma includes neoadjuvant chemotherapy followed by surgical resection and postoperative chemotherapy. The percentage of necrotic tissue following induction chemotherapy is assessed by using the Huvos grading system, which classifies patients as "poor responders" (PR) and "good responders" (GR). The aim of this study was to identify molecular markers expressed differentially between good and poor responders to neoadjuvant chemotherapy in order to predict the response to chemotherapy in conventional osteosarcomas before beginning treatment., Methods: Suppression Substractive Hybridization (SSH) was performed by using cDNA from frozen biopsy specimens. Expression of selected relevant genes identified by SSH was validated by using QRT-PCR. Immunohistochemistry (IHC) on tissue microarray (TMA) sections of 52 biopsies was performed to investigate protein expression in an independent cohort., Results: ERK1 and STAT3 mRNA level were significantly different between PR and GR in an independent cohort. Phosphorylated STAT3 and ERK1 expressions by IHC on TMA were correlated with poor response to chemotherapy., Conclusions: Our results suggest that ERK1 and STAT3 expression are good predictive markers for chemotherapy response and that inhibitors might be used in combination with common chemotherapeutic drugs in conventional osteosarcomas.

Novel prognostic biomarkers are imperatively needed to help direct treatment decisions by typing subgroups of node-negative breast cancer patients. Large screening of different biological compartments, such as the proteome, by means of high throughput techniques may greatly help scientists to find such markers. The present retrospective multicentric study included 268 node-negative breast cancer patients. We used a proteomic approach of SELDI-TOF-MS screening to identify differentially expressed cytosolic proteins with prognostic impact. The screening cohort was composed of 198 patients. Seventy supplementary patients were included for validation. Immunohistochemistry (IHC) and immunoassay (IA) were run to confirm the prognostic role of the marker identified by SELDI-TOF-MS screening. IHC was also used to explore links between selected marker and epithelial-mesenchymal transition (EMT)-like, proliferation and macrophage markers. Ferritin light chain (FTL) was identified as an independent prognostic marker (HR = 1.30-95% CI: 1.10-1.50, p = 0.001). Validation step by means of IHC and IA confirmed the prognostic value of FTL level. CD68 IHC showed that FTL was stored in tumor-associated macrophages (TAM), which exhibit an M2-like phenotype. We report here, first, the validation of FTL as a breast tumor prognostic biomarker in node-negative patients, and second, the fact that FTL is stored in TAM., (Copyright © 2011 UICC.)

We used a 2D-electrophoresis (2-DE) proteomic approach to identify novel biomarkers in node-negative breast cancers. This retrospective study focused on a population of patients with ductal pN0M0 tumours. A subset of patients who developed metastases and in whose tumours were found high levels of uPA and PAI-1 (metastatic relapse, MR: n=20) were compared to another subset in whom no metastatic relapse occurred and whose tumours were found to have low levels of uPA and PAI-1 (no relapse, NR: n=21). We used a 2-DE coupled with MS approach to screen cytosol fractions using two pH-gradient scales, a broad scale (3.0-11.0) and a narrower scale focussing in on a protein rich region (5.0-8.0). This study was conducted on 41 cytosol specimens analyzed in duplicate on two platforms. The differential analysis of more than 2,000 spots in 2-DE gels, obtained on the two platforms, allowed the identification of 13 proteins which were confirmed by western blotting. Two proteins, GPDA and FABP4 were down-regulated in the MR subset whereas all the others were up-regulated. An in silico analysis revealed that GMPS (GUAA), GAPDH (G3P), CFL1 (COF1) and FTL (FRIL), the most informative genes, displayed a proliferation profile (high expression in basal-like, HER2+ and luminal B molecular subtypes). Inversely, similar to FABP4, GPD1 [GPDA] displayed a high expression in luminal A subtype, a profile characteristic of tumour suppressor genes. Despite the small size of our cohort, the 2-DE analysis gave interesting results which were confirmed by the in silico analysis showing that some of the corresponding genes had a strong prognostic impact in breast cancer, mostly because of their link with proliferation: GMPS, GAPDH, FTL and GPD1. A validation phase on a larger cohort is now needed before these biomarkers could be considered for use in clinical practice.

The therapy regimen of high-grade osteosarcoma includes chemotherapy followed by surgical resection and postoperative chemotherapy. The degree of necrosis following definitive surgery remains the only reliable prognostic factor and is used to guide the choice of postoperative chemotherapy. The aim of this study was to find molecular markers able to classify patients with an osteosarcoma as good or poor responders to chemotherapy before beginning treatment. Gene expression screening of 20 nonmetastatic high-grade osteosarcoma patients was performed using cDNA microarray. Expression of selected relevant genes was validated using QRT-PCR. Immunohistochemistry on tissue microarrays sections of 73 biopsies was performed to investigate protein expression. Fluorescent in situ hybridization was performed for RPL8 gene. We have found that HSD17B10 gene expression was up-regulated in poor responders and that immunohistochemistry expression of HSD17B10 on biopsy before treatment was correlated to response to chemotherapy. Other results include correlation of IFITM2, IFITM3, and RPL8 gene expression to chemotherapy response. A statistical correlation was found between polysomy 8 or gain of RPL8 and good response to chemotherapy. These data suggest that HSD17B10, RPL8, IFITM2, and IFITM3 genes are involved in the response to the chemotherapy and that HSD17B10 may be a therapeutic target. RPL8 and IFITM2 may be useful in the assessment at diagnosis and for stratifying patients taking part in randomized trials.

Currently, no prognostic gene-expression signature (GES) established from node-positive breast cancer cohorts, able to predict evolution after systemic adjuvant chemotherapy, exists. Gene-expression profiles of 252 node-positive breast cancer patients (median follow-up: 7.7 years), mostly included in a randomized clinical trial (PACS01), receiving systemic adjuvant regimen, were determined by means of cDNA custom array. In the training cohort, we established a GES composed of 38 genes (38-GES) for the purpose of predicting metastasis-free survival. The 38-GES yielded unadjusted hazard ratio (HR) of 4.86 (95% confidence interval = 2.76-8.56). Even when adjusted with the best two clinicopathological prognostic indexes: Nottingham prognostic index (NPI) and Adjuvant!, 38-GES HRs were 3.30 (1.81-5.99) and 3.40 (1.85-6.24), respectively. Furthermore, 38-GES improved NPI and Adjuvant! classification. In particular, NPI intermediate-risk patients were divided into 2/3 close to low-risk group and 1/3 close to high-risk group (HR = 6.97 [2.51-19.36]). Similarly, Adjuvant! intermediate-risk patients were divided into 2/3 close to low-risk group and 1/3 close to high-risk group (HR = 4.34 [1.64-11.48]). The 38-GES was validated on gene-expression datasets from three external node-positive breast cancer subcohorts (n = 224) generated from different microarray platforms, with HR = 2.95 (1.74-5.01). Moreover, 38-GES showed prognostic performance in supplementary cohorts with different lymph-node status and endpoints (1,040 new patients). The 38-GES represents a robust tool able to type systemic adjuvant treated node-positive patients at high risk of metastatic relapse, and is especially powerful to refine NPI and Adjuvant! classification for those patients.

Novel prognostic biomarkers are imperatively needed to help direct treatment decisions by typing subgroups of node-negative breast cancer patients. The current study has used a proteomic approach of SELDI-TOF-MS screening to identify differentially cytosolic expressed proteins with a prognostic impact in 30 node-negative breast cancer patients with no relapse versus 30 patients with metastatic relapse. The data analysis took into account 73 peaks, among which 2 proved, by means of univariate Cox regression, to have a good cumulative prognostic-informative power. Repeated random sampling (n = 500) was performed to ensure the reliability of the peaks. Optimized thresholds were then computed to use both peaks as risk factors and, adding them to the St. Gallen ones, improve the prognostic classification of node-negative breast cancer patients. Identification of ubiquitin and ferritin light chain (FLC), corresponding to the two peaks of interest, was obtained using ProteinChip LDI-Qq-TOF-MS. Differential expression of the two proteins was further confirmed by Western blotting analyses and immunohistochemistry. SELDI-TOF-MS protein profiling clearly showed that a high level of cytosolic ubiquitin and/or a low level of FLC were associated with a good prognosis in breast cancer.