Validation of an NGS mutation detection panel for melanoma.

BACKGROUND: Knowledge of the genotype of melanoma is important to guide patient management. Identification of mutations in BRAF and c-KIT lead directly to targeted treatment, but it is also helpful to know if there are driver oncogene mutations in NRAS, GNAQ or GNA11 as these patients may benefit from alternative strategies such as immunotherapy. METHODS: While polymerase chain reaction (PCR) methods are often used to detect BRAF mutations, next generation sequencing (NGS) is able to determine all of the necessary information on several genes at once, with potential advantages in turnaround time. We describe here an Ampliseq hotspot panel for melanoma for use with the IonTorrent Personal Genome Machine (PGM) which covers the mutations currently of most clinical interest. RESULTS: We have validated this in 151 cases of skin and uveal melanoma from our files, and correlated the data with PCR based assessment of BRAF status. There was excellent agreement, with few discrepancies, though NGS does have greater coverage and picks up some mutations that would be missed by PCR. However, these are often rare and of unknown significance for treatment. CONCLUSIONS: PCR methods are rapid, less time-consuming and less expensive than NGS, and could be used as triage for patients requiring more extensive diagnostic workup. The NGS panel described here is suitable for clinical use with formalin-fixed paraffin-embedded (FFPE) samples.

The evidence base for circulating tumour DNA blood-based biomarkers for the early detection of cancer: a systematic mapping review.

BACKGROUND: The presence of circulating cell-free DNA from tumours in blood (ctDNA) is of major importance to those interested in early cancer detection, as well as to those wishing to monitor tumour progression or diagnose the presence of activating mutations to guide treatment. In 2014, the UK Early Cancer Detection Consortium undertook a systematic mapping review of the literature to identify blood-based biomarkers with potential for the development of a non-invasive blood test for cancer screening, and which identified this as a major area of interest. This review builds on the mapping review to expand the ctDNA dataset to examine the best options for the detection of multiple cancer types. METHODS: The original mapping review was based on comprehensive searches of the electronic databases Medline, Embase, CINAHL, the Cochrane library, and Biosis to obtain relevant literature on blood-based biomarkers for cancer detection in humans (PROSPERO no. CRD42014010827). The abstracts for each paper were reviewed to determine whether validation data were reported, and then examined in full. Publications concentrating on monitoring of disease burden or mutations were excluded. RESULTS: The search identified 94 ctDNA studies meeting the criteria for review. All but 5 studies examined one cancer type, with breast, colorectal and lung cancers representing 60% of studies. The size and design of the studies varied widely. Controls were included in 77% of publications. The largest study included 640 patients, but the median study size was 65 cases and 35 controls, and the bulk of studies (71%) included less than 100 patients. Studies either estimated cfDNA levels non-specifically or tested for cancer-specific mutations or methylation changes (the majority using PCR-based methods). CONCLUSION: We have systematically reviewed ctDNA blood biomarkers for the early detection of cancer. Pre-analytical, analytical, and post-analytical considerations were identified which need to be addressed before such biomarkers enter clinical practice. The value of small studies with no comparison between methods, or even the inclusion of controls is highly questionable, and larger validation studies will be required before such methods can be considered for early cancer detection.

Inhibition of PI3K Class IA Kinases Using GDC-0941 Overcomes Cytoprotection of Multiple Myeloma Cells in the Osteoclastic Bone Marrow Microenvironment Enhancing the Efficacy of Current Clinical Therapeutics.

Osteoclasts contribute to bone marrow (BM)-mediated drug resistance in multiple myeloma (MM) by providing cytoprotective cues. Additionally, 80% of patients develop osteolytic lesions, which is a major cause of morbidity in MM. Although targeting osteoclast function is critical to improve MM therapies, pre-clinical studies rarely consider overcoming osteoclast-mediated cytoprotection within the selection criteria of drug candidates. We have performed a drug screening and identified PI3K as a key regulator of a signalling node associated with resistance to dexamethasone lenalidomide, pomalidomide, and bortezomib mediated by osteoclasts and BM fibroblastic stromal cells, which was blocked by the pan-PI3K Class IA inhibitor GDC-0941. Additionally, GDC-0941 repressed the maturation of osteoclasts derived from MM patients and disrupted the organisation of the F-actin cytoskeleton in sealing zones required for bone degradation, correlating with decreased bone resorption by osteoclasts. In vivo, GDC-0941 improved the efficacy of dexamethasone against MM in the syngeneic GFP-5T33/C57-Rawji mouse model. Taken together, our results indicate that GDC-0941 in combination with currently used therapeutic agents could effectively kill MM cells in the presence of the cytoprotective BM microenvironment while inhibiting bone resorption by osteoclasts. These data support investigating GDC-0941 in combination with currently used therapeutic drugs for MM patients with active bone disease.

Making sense of molecular signatures in the immune system.

The development of Functional Genomics technologies has opened new avenues to investigate the complexity of the immune system. Microarray technology has been particularly successful because of its relatively low cost and high genome coverage. Consequently to our ability to monitor the expression of a significant proportion of an organism genome, our understanding of the molecular dynamics behind cell differentiation and cell response has greatly improved. Molecular signatures associated to immune cells have provided important tools to investigate the molecular basis of diseases and have been often associated to diagnostic and prognostic markers. The availability of such large collection of data has stimulated the application of complex machine learning techniques in the attempt to link molecular signatures and cell physiology. Here we review the most recent developments in the analysis of molecular signatures in the immune system.