The utilisation of digital droplet PCR to enhance the diagnosis of bladder and pancreaticobiliary tumours in cytology specimens.

Background: Digital droplet PCR (ddPCR) is a relatively new technique used to detect molecular alterations with unprecedented precision and accuracy. It is particularly useful for detecting point mutations and copy number variation (CNV) in samples with small amounts of target DNA. This proof of principle study was conducted to see if ddPCR technology could be applied to cytology specimens to detect molecular alterations which may influence diagnostic decision making.

Engineered in-vitro cell line mixtures and robust evaluation of computational methods for clonal decomposition and longitudinal dynamics in cancer.

Characterization and quantification of tumour clonal populations over time via longitudinal sampling are essential components in understanding and predicting the response to therapeutic interventions. Computational methods for inferring tumour clonal composition from deep-targeted sequencing data are ubiquitous, however due to the lack of a ground truth biological data, evaluating their performance is difficult. In this work, we generate a benchmark data set that simulates tumour longitudinal growth and heterogeneity by in vitro mixing of cancer cell lines with known proportions.