Characterization of Macrophages and Osteoclasts in the Osteosarcoma Tumor Microenvironment at Diagnosis: New Perspective for Osteosarcoma Treatment?

Biological and histopathological techniques identified osteoclasts and macrophages as targets of zoledronic acid (ZA), a therapeutic agent that was detrimental for patients in the French OS2006 trial. Conventional and multiplex immunohistochemistry of microenvironmental and OS cells were performed on biopsies of 124 OS2006 patients and 17 surgical (“OSNew”) biopsies respectively. CSF-1R (common osteoclast/macrophage progenitor) and TRAP (osteoclast activity) levels in serum of 108 patients were correlated to response to chemotherapy and to prognosis. TRAP levels at surgery and at the end of the protocol were significantly lower in ZA+ than ZA− patients (padj = 0.0011; 0.0132). For ZA+-patients, an increase in the CSF-1R level between diagnosis and surgery and a high TRAP level in the serum at biopsy were associated with a better response to chemotherapy (p = 0.0091; p = 0.0251). At diagnosis, high CD163+ was associated with good prognosis, while low TRAP activity was associated with better overall survival in ZA− patients only. Multiplex immunohistochemistry demonstrated remarkable bipotent CD68+/CD163+ macrophages, homogeneously distributed throughout OS regions, aside osteoclasts (CD68+/CD163−) mostly residing in osteolytic territories and osteoid-matrix-associated CD68−/CD163+ macrophages. We demonstrate that ZA not only acts on harmful osteoclasts but also on protective macrophages, and hypothesize that the bipotent CD68+/CD163+ macrophages might present novel therapeutic targets

Caractères phénotypiques et génomiques du parenchyme mammaire des femmes porteuses de mutations des gènes BRCA1 ou BRCA2

PARIS6-Bibl.Pitié-Salpêtrie (751132101) / SudocPARIS-BIUM (751062103) / SudocSudocFranceF

Imagerie du sein : Correlations radio-histologiques Villejuif, 14-15 decembre 2000

Available from INIST (FR), Document Supply Service, under shelf-number : Y 33194 / INIST-CNRS - Institut de l'Information Scientifique et TechniqueSIGLEInstitut Gustave Roussy, 94 - Villejuif (France)FRFranc

Total metabolic tumor volume and spleen metabolism on baseline [18F]-FDG PET/CT as independent prognostic biomarkers of recurrence in resected breast cancer

International audiencePurpose We evaluated whether biomarkers on baseline [ 18 F]-FDG PET/CT are associated with recurrence after surgery in patients with invasive breast cancer of no special type (NST). Methods In this retrospective single-center study, we included consecutive patients with non-metastatic breast cancer of NST who underwent [ 18 F]-FDG PET/CT before treatment, including surgery, between 2011 and 2016. Clinicopathological data were collected. Tumor SUVmax, total metabolic tumor volume (TMTV), and spleen-and bone marrow-to-liver SUVmax ratios (SLR, BLR) were measured from the PET images. Cutoff values were determined using predictiveness curves to predict 5-year recurrence-free survival (5y-RFS). A multivariable prediction model was developed using Cox regression. The association with stromal tumor-infiltrating lymphocytes (TILs) levels (low if 20 cm3) and high SLR (>0.76) were associated with shorter 5y-RFS (HR 2.4, 95%CI 1.3-4.5, and HR 1.9, 95%CI 1.0-3.6). In logistic regression, high SLR was the only independent factor associated with low stromal TILs (OR 2.8, 95%CI 1.4-5.7). Conclusion High total metabolic tumor volume and high spleen glucose metabolism on baseline [ 18 F]-FDG PET/CT were associated with poor 5y-RFS after surgical resection in patients with breast cancer of NST. Spleen metabolism was inversely correlated with stromal TILs and might be a surrogate for an immunosuppressive tumor microenvironment. Keywords Invasive breast cancer of no special type. [18F]-FDG PET/CT. Prognosis. Total metabolic tumor volume. Spleen glucose metabolism. Stromal tumor-infiltrating lymphocytes This article is part of the Topical Collection on Oncology-Genera

Non-sentinel lymph node metastasis prediction in breast cancer with metastatic sentinel lymph node: impact of molecular subtypes classification.

INTRODUCTION: To decipher the interaction between the molecular subtype classification and the probability of a non-sentinel node metastasis in breast cancer patients with a metastatic sentinel lymph-node, we applied two validated predictors (Tenon Score and MSKCC Nomogram) on two large independent datasets. MATERIALS AND METHODS: Our datasets consisted of 656 and 574 early-stage breast cancer patients with a metastatic sentinel lymph-node biopsy treated at first by surgery. We applied both predictors on the whole dataset and on each molecular immune-phenotype subgroups. The performances of the two predictors were analyzed in terms of discrimination and calibration. Probability of non-sentinel lymph node metastasis was detailed for each molecular subtype. RESULTS: Similar results were obtained with both predictors. We showed that the performance in terms of discrimination was as expected in ER Positive HER2 negative subgroup in both datasets (MSKCC AUC Dataset 1 = 0.73 [0.69-0.78], MSKCC AUC Dataset 2 = 0.71 (0.65-0.76), Tenon Score AUC Dataset 1 = 0.7 (0.65-0.75), Tenon Score AUC Dataset 2 = 0.72 (0.66-0.76)). Probability of non-sentinel node metastatic involvement was slightly under-estimated. Contradictory results were obtained in other subgroups (ER negative HER2 negative, HER2 positive subgroups) in both datasets probably due to a small sample size issue. We showed that merging the two datasets shifted the performance close to the ER positive HER2 negative subgroup. DISCUSSION: We showed that validated predictors like the Tenon Score or the MSKCC nomogram built on heterogeneous population of breast cancer performed equally on the different subgroups analyzed. Our present study re-enforce the idea that performing subgroup analysis of such predictors within less than 200 samples subgroup is at major risk of misleading conclusions

A six-gene signature predicting breast cancer lung metastasis.

The lungs are a frequent target of metastatic breast cancer cells, but the underlying molecular mechanisms are unclear. All existing data were obtained either using statistical association between gene expression measurements found in primary tumors and clinical outcome, or using experimentally derived signatures from mouse tumor models. Here, we describe a distinct approach that consists of using tissue surgically resected from lung metastatic lesions and comparing their gene expression profiles with those from nonpulmonary sites, all coming from breast cancer patients. We show that the gene expression profiles of organ-specific metastatic lesions can be used to predict lung metastasis in breast cancer. We identified a set of 21 lung metastasis-associated genes. Using a cohort of 72 lymph node-negative breast cancer patients, we developed a 6-gene prognostic classifier that discriminated breast primary cancers with a significantly higher risk of lung metastasis. We then validated the predictive ability of the 6-gene signature in 3 independent cohorts of breast cancers consisting of a total of 721 patients. Finally, we show that the signature improves risk stratification independently of known standard clinical variables and a previously established lung metastasis signature based on an experimental breast cancer metastasis model

Response to mTOR and PI3K inhibitors in enzalutamide-resistant luminal androgen receptor triple-negative breast cancer patient-derived xenografts

Response to mTOR and PI3K inhibitors in enzalutamide-resistant luminal androgen receptor triple-negative breast cancer patient-derived xenograft