Molecular diagnostics and personalized medicine in oncology: Challenges and opportunities

Abstract

Increasing evidence demonstrates that target-based agents are active only in molecularly selected populations of patients. Therefore, the identification of predictive biomarkers has become mandatory to improve the clinical development of these novel drugs. Mutations of the epidermal growth factor receptor (EGFR) or rearrangements of the ALK gene in non-small-cell lung cancer, and BRAF mutations in melanoma are clear examples of driver mutations and predictive biomarkers of response to treatment with specific inhibitors. Predictive biomarkers might also identify subgroups of patients that are not likely to respond to specific drugs, as shown for KRAS mutations and anti-EGFR monoclonal antibodies in colorectal carcinoma. The discovery of novel driver molecular alterations and the availability of drugs capable to selectively block such oncogenic mechanisms are leading to a rapid increase in the number of putative biomarkers that need to be assessed in each single patient. In this respect, two different approaches are being developed to introduce a comprehensive molecular characterization in clinical practice: high throughput genotyping platforms, which allow the detection of recognized genetic aberrations in clinical samples, and next generation sequencing that can provide information on all the different types of cancer-causing alterations. The introduction of these techniques in clinical practice will increase the possibility to identify molecular targets in each individual patient, and will also allow to follow the molecular evolution of the disease during the treatment. By using these approaches, the development of personalized medicine for patients with cancer will finally become possible.

Assessment of High-Sensitive Methods for the Detection of EGFR Mutations in Circulating Free Tumor DNA from NSCLC Patients

“…Importantly, the thresholds indicated by the manufacturer for the Therascreen kit have been established to analyse DNA from formalin fixed paraffin embedded tissues. DNA extracted from formalin fixed paraffin embedded tissues is often poorly amplifiable and might contain artifacts introduced by the fixation process [28]. In this respect, DNA from plasma or from cell lines usually shows a better profile of amplification and a lower background that might explain the high sensitivity that we report in this study.…”

Applications of Support Vector Machine (SVM) Learning in Cancer Genomics

“…The continuing decline in the cost of genome sequencing, as well as the relative ease of interpreting the effects of mutations in many proteins via methods such as activity assays has led to a sustained drive to understand the effects of cancer derived mutations on cancer progression. The challenge of finding mechanistic links between mutations and cancer progression is made even more imperative by the fact that many cancer drugs target mutations that have specific effects, as well as the observation that many clinical trials fail due to patient cohorts that are not suitable for specific therapies (57). Sequencing efforts as well as the frequent failure of targeted therapies has led to an increasingly well-recognized principle that not all mutations confer selective advantage on cancer cells.…”

An improved fluorescence polarization assay in 5'-nuclease reaction for gene promoter methylation detection

“…Transcriptional inactivation by cytosine-5 methylation at promoter CpG islands of tumor suppressor genes is thought to be an important mechanism in human carcinogenesis. Aberrant cytosine-5 methylation at gene promoter CpG islands results in cancer progression and chemotherapy resistance (How Kit et al, 2012;Normanno et al, 2013;Ren et al,2011). The detection of gene promoter methylation plays increasing roles in personalized medicine (Mikeska et al, 2012).…”