Molecular and clinicopathological markers of prognosis in breast cancer.

A vast body of research in breast cancer prognostication has accumulated. Yet despite this, patients within current prognostic categories may have significantly different outcomes. There is a need to more accurately divide those cancer types associated with an excellent prognosis from those requiring more aggressive therapy. Gene expression array studies have revealed the numerous molecular breast cancer subtypes that are associated with differing outcomes. Furthermore, as next generation technologies evolve and further reveal the complexities of breast cancer, it is likely that existing prognostic approaches will become progressively refined. Future prognostication in breast cancer requires a morphomolecular, multifaceted approach involving the assessment of anatomical disease extent and levels of protein, DNA and RNA expression. One of the major challenges in prognostication will be the integration of potential assays into existing clinical systems and identification of appropriate patient subgroups for analysis.

Validation of next generation sequencing technologies in comparison to current diagnostic gold standards for BRAF, EGFR and KRAS mutational analysis.

Next Generation Sequencing (NGS) has the potential of becoming an important tool in clinical diagnosis and therapeutic decision-making in oncology owing to its enhanced sensitivity in DNA mutation detection, fast-turnaround of samples in comparison to current gold standard methods and the potential to sequence a large number of cancer-driving genes at the one time. We aim to test the diagnostic accuracy of current NGS technology in the analysis of mutations that represent current standard-of-care, and its reliability to generate concomitant information on other key genes in human oncogenesis. Thirteen clinical samples (8 lung adenocarcinomas, 3 colon carcinomas and 2 malignant melanomas) already genotyped for EGFR, KRAS and BRAF mutations by current standard-of-care methods (Sanger Sequencing and q-PCR), were analysed for detection of mutations in the same three genes using two NGS platforms and an additional 43 genes with one of these platforms. The results were analysed using closed platform-specific proprietary bioinformatics software as well as open third party applications. Our results indicate that the existing format of the NGS technology performed well in detecting the clinically relevant mutations stated above but may not be reliable for a broader unsupervised analysis of the wider genome in its current design. Our study represents a diagnostically lead validation of the major strengths and weaknesses of this technology before consideration for diagnostic use.