The Non-Bone-Related Role of RANK/RANKL Signaling in Cancer.

RANK ligand (RANKL) is a member of the tumor necrosis factor alpha superfamily of cytokines. It is the only known ligand binding to a membrane receptor named receptor activator of nuclear factor-kappa B (RANK), thereby triggering recruitment of TNF receptor-associated factor (TRAF) adaptor proteins and activation of downstream pathways. RANK/RANKL signaling is controlled by a decoy receptor, osteoprotegerin (OPG), but also has additional more complex levels of regulation. It is crucial for the differentiation of bone-resorbing osteoclasts and is deregulated in disease processes such as osteoporosis and cancer bone metastasis. Cells expressing RANK and RANKL are commonly found in the tumor environment. In many tumor types, the RANK/RANKL pathway is overexpressed, and this is in most cases correlated with poor prognosis. RANK signaling plays an important role in the innate and adaptive immune response, generates regulatory T (Treg) cells, and increases the production of cytokines. It is also involved in chemo resistance in vitro. Recent evidence suggests that RANKL blockade improves the efficacy of anti-CTLA-4 antibodies against solid tumors and experimental metastasis. Therefore, there is increasing interest to use RANKL inhibition as an immunomodulatory strategy in an attempt to make immune-resistant tumor responsive to immune therapy.

RANK/RANKL signaling inhibition may improve the effectiveness of checkpoint blockade in cancer treatment.

Binding between the receptor activator of nuclear factor-kB (RANK) and its ligand (RANKL) triggers recruitment of TNF receptor associated factor (TRAF) adaptor proteins and activation of downstream pathways. RANK/RANKL signaling is controlled by a decoy receptor called osteoprotegerin (OPG) which interacts with RANKL. Additional networks regulating RANK/RANKL signaling are active in a context specific manner. RANK/RANKL signaling is essential for the differentiation of bone-resorbing osteoclasts, and is deregulated in pathological processes such as postmenopausal osteoporosis or cancer induced bone destruction. Cells expressing RANK and RANKL are commonly found in the tumor microenvironment. The RANKL/RANK pathway is often overexpressed in tumors of the breast, prostate, endometrium, cervix, stomach, oesophagus and bladder, thyroid and correlated with poor prognosis. RANK signaling plays an important role in the innate and adaptive immune response as it generates regulatory T (Treg) cells and increases production of cytokines. RANK expression induces chemoresistance in vitro through the activation of multiple signal transduction pathways. RANKL blockade improves the efficacy of anti-CTLA-4 monoclonal antibodies against solid tumors and experimental metastases. As RANK inhibition enhances the immune response there is an increasing interest in combining it with immune therapy in an attempt to sensitize immune resistant tumors to immune therapies. Several studies are ongoing to assess this concept. The role of RANK/RANKL inhibition should be further pursued as an immunomodulatory strategy in combination with other treatment modalities.

A dynamic clinical pathway for the treatment of patients with early breast cancer is a tool for better cancer care: implementation and prospective analysis between 2002-2010.

BACKGROUND: Due to increasing the complexity of breast cancer treatment it is of paramount importance to develop structured care in order to avoid a chaotic and non-consistent management of patients. Clinical pathways, a result of the adaptation of the documents used in industrial quality management namely the Standard Operating Procedures, can be used to improve efficiency and quality of care. They also aim to re-centre the focus on the patient's overall journey, rather than the contribution of each specialty or caring function independently. METHODS: The effect of the implementation and prospective systematic evaluation of a clinical care pathway for the management of patients with early breast cancer in a single breast unit is evaluated over a long time interval (between 2002 and 2010). Annual analysis of predefined clinical outcome measures, service indicators, team indicators, process indicators and financial indicators was performed. Pathway quality control meetings were organized at least once a year. Systematic feedback was given to the team members, and if necessary the pathway was adapted according to evidence based literature data and in house pathway related data in order to improve quality. RESULTS: The annual number of patients included in the pathway (289 vs. 390, P <0.01), proportion of patients with Tis-T1 tumors (42% vs. 58%, P <0.01), negative lymph nodes (44% vs. 58%, P <0.01) and no metastases at diagnosis (91.5% vs. 95.9%) has risen significantly between 2002 and 2010. Evolution of mandatory quality indicators defined by EUSOMA shows a significant improvement of quality of cancer care. Particularly, the proportion of patients having anti-hormonal therapy (84.8% vs. 97.4%, P = 0.002) and adjuvant chemotherapy according to the guidelines (72% vs. 95.6%, P = 0.028) increased dramatically. Patient satisfaction improved significantly (P <0.05). Progression free 4-year survival was significantly higher for all patients, for T1 tumors only and for T2-T4 tumors only, treated between 2006 to 2008 compared to between 1999 to 2002 and 2003 to 2005 (P = 0.006, P = 0.05, P = 0.06, respectively). Overall 4-year survival of the entire population treated between 2006 and 2008 was significantly better (P = 0.05). CONCLUSIONS: Although the patient characteristics changed over the years due to better screening, this clinical pathway and regular audit of quality indicators for the treatment of patients with operable breast cancer proved to be important tools to improve the quality of care, patient satisfaction and outcome.

Quantitative methylation profiling in tumor and matched morphologically normal tissues from breast cancer patients.

BACKGROUND: In the present study, we determined the gene hypermethylation profiles of normal tissues adjacent to invasive breast carcinomas and investigated whether these are associated with the gene hypermethylation profiles of the corresponding primary breast tumors. METHODS: A quantitative methylation-specific PCR assay was used to analyze the DNA methylation status of 6 genes (DAPK, TWIST, HIN-1, RASSF1A, RARbeta2 and APC) in 9 normal breast tissue samples from unaffected women and in 56 paired cancerous and normal tissue samples from breast cancer patients. RESULTS: Normal tissue adjacent to breast cancer displayed statistically significant differences to unrelated normal breast tissues regarding the aberrant methylation of the RASSF1A (P = 0.03), RARbeta2 (P = 0.04) and APC (P = 0.04) genes. Although methylation ratios for all genes in normal tissues from cancer patients were significantly lower than in the cancerous tissue from the same patient (P < or = 0.01), in general, a clear correlation was observed between methylation ratios measured in both tissue types for all genes tested (P < 0.01). When analyzed as a categorical variable, there was a significant concordance between methylation changes in normal tissues and in the corresponding tumor for all genes tested but RASSF1A. Notably, in 73% of patients, at least one gene with an identical methylation change in cancerous and normal breast tissues was observed. CONCLUSIONS: Histologically normal breast tissues adjacent to breast tumors frequently exhibit methylation changes in multiple genes. These methylation changes may play a role in the earliest stages of the development of breast neoplasia.

Quantitative assessment of DNA hypermethylation in the inflammatory and non-inflammatory breast cancer phenotypes.

In this study, a comparative quantitative methylation profiling of inflammatory breast cancer (IBC) and non-IBC was set up for the identification of tumor-specific methylation patterns. Methylation ratios of six genes (DAPK, TWIST, HIN-1, RASSF1A, RARbeta2 and APC) were measured in benign breast tissues (n = 9) and in tumor samples from non-IBC (n = 81) and IBC (n = 19) patients using quantitative methylation-specific PCR. Median methylation ratios observed in breast cancer (n = 100) were significantly higher than those observed in benign breast tissues for five of six genes (TWIST, HIN-1, RASSF1A, RARbeta2 and APC). Only one of the individual genes studied, RARbeta2, showed differential methylation ratios in IBC and non-IBC (p = 0.016). Using the maximal methylation ratio observed in benign breast tissue as a threshold, the methylation frequency of two genes, RARbeta2 and APC, was significantly increased in IBC (n = 19) when compared to non-IBC (n = 81): 53 vs. 23% for RARbeta2 (p = 0.012) and 84 vs. 54% for APC (p = 0.017). Using hierarchical clustering, methylation patterns could not classify breast cancers according to their phenotype. The finding of differential frequencies of methylation in IBC and non-IBC for two out of six genes suggests that gene-specific patterns of methylation could provide a basis for molecular classification of IBC. Testing for additional genes could help to define the IBC phenotype based on patterns of aberrant gene promoter methylation.