[Show abstract][Hide abstract] ABSTRACT: Global gene expression analysis was performed on pre-treatment biopsies from patients with squamous cell carcinoma of the head and neck (SCCHN) to discover biomarkers that can predict outcome of radiation based therapy.
We initially evaluated RNA expression using cDNA microarray analysis of 38 patients that received radiotherapy (RT). The five strongest candidates (VEGF, BCL-2, CLAUDIN-4, YAP-1 and c-MET) were then analysed in pre-treatment biopsies in a second group of 86 patients who received radiation based treatment using immunohistochemical staining (IHC), prepared by tissue microarray.
In the first population, 13 of 38 (34%) had no (NR) or partial response (PR) to RT. cDNA microarrays revealed 60 genes that were linked to response to therapy. In the second series, 12 of 86 patients (14%) experienced NR or PR to CRT. Cause specific survival (CSS) and recurrence free survival (RFS) at 2 years was 85% and 90% and at 3 years 81% and 84%, respectively. Biomarkers predictive for NR/PR were increased expression of vascular endothelial growth factor (VEGF) (p=0.02), Yes-associated protein (YAP-1) (p<0.01), CLAUDIN-4 (p<0.01), c-MET (p<0.01) and BCL-2 (p=0.02). Biomarkers predictive of poor RFS were YAP-1 (p=0.01) and BCL-2 (p<0.01). Biomarkers predictive of poor CSS were YAP-1 (p=0.04), VEGF (p=0.03) and CLAUDIN-4 (p=0.03). Furthermore, when YAP-1 and c-MET expression levels were combined the prediction of radio-resistance was increased.
All five biomarkers were predictive of poor response to radiation based therapy. In particular, YAP-1 and c-MET have synergistic power and could be used to make treatment decisions.
European journal of cancer (Oxford, England: 1990) 12/2013; · 4.12 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Although the same simple laws govern cancer outcome (cell division repeated again and again), each tumour has a different outcome before as well as after irradiation therapy. The linear-quadratic radiosensitivity model allows an assessment of tumor sensitivity to radiotherapy. This model presents some limitations in clinical practice because it does not take into account the interactions between tumour cells and non-tumoral bystander cells (such as endothelial cells, fibroblasts, immune cells…) that modulate radiosensitivity and tumor growth dynamics. These interactions can lead to non-linear and complex tumor growth which appears to be random but that is not since there is not so many tumors spontaneously regressing. In this paper we propose to develop a deterministic approach for tumour growth dynamics using chaos theory. Various characteristics of cancer dynamics and tumor radiosensitivity can be explained using mathematical models of competing cell species.
[Show abstract][Hide abstract] ABSTRACT: The Met receptor tyrosine kinase (RTK) is an attractive oncology therapeutic target. Met and its ligand, HGF, play a central role in signaling pathways that are exploited during the oncogenic process, including regulation of cell proliferation, invasion, angiogenesis, and cancer stem cell regulation. Elevated Met and HGF as well as numerous Met genetic alterations have been reported in human cancers and correlate with poor outcome. Alterations of pathways that regulate Met, such as the ubiquitin ligase c-Cbl are also likely to activate Met in the oncogenic setting. Moreover, interactive crosstalk between Met and other receptors such as EGFR, HER2 and VEGFR, underlies a key role for Met in resistance to other RTK-targeted therapies. A large body of preclinical and clinical data exists that supports the use of either antibodies or small molecule inhibitors that target Met or HGF as oncology therapeutics. The prognostic potential of Met expression has been suggested from studies in numerous cancers including lung, renal, liver, head and neck, stomach, and breast. Clinical trials using Met inhibitors indicate that the level of Met expression is a determinant of trial outcome, a finding that is actively under investigation in multiple clinical scenarios. Research in Met prognostics and predictors of drug response is now shifting toward more sophisticated methodologies suitable for development as validated and effective biomarkers that can be partnered with therapeutics to improve patient survival.
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