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. METHODS: A range of cytological specimens derived from bladder or pancreaticobiliary origin, with varying diagnoses but with an emphasis on abnormality, underwent ddPCR testing. DNA was manually extracted from Thinprep vials, cell blocks or direct fine needle aspiration smears. ddPCR was performed using two somatic point mutations TP53 R248W and TP53 R273H assays for both urine and pancreaticobiliary cytology specimens. Three CNV assays; CDKN2A, E2F3 and YWHAZ were applied to urine samples. SMAD4 and CDKN2A CNVs were applied to the pancreaticobiliary samples. RESULTS: 16 of 21 urine specimens showed molecular alterations using ddPCR testing. 12 of those 16 cases were associated with malignant outcomes. The pancreaticobiliary specimens showed 14 of 37 specimens with molecular alterations, all of which were associated with carcinoma. We demonstrated an increasing percentage of molecular aberrations associated with increasing severity of cytological results. CONCLUSION: Our results have shown that ddPCR can identify both mutations and CNVs in urine and pancreaticobiliary cytology derived samples. Being able to detect these molecular alterations may reduce the number of equivocal results leading to more timely and informed patient management decisions.

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. We apply four different algorithms to our ground truth data set and assess their performance in inferring clonal composition using different metrics. We also analyse the performance of these algorithms on breast tumour xenograft samples. We conclude that methods that can simultaneously analyse multiple samples while accounting for copy number alterations as a factor in allelic measurements exhibit the most accurate predictions. These results will inform future functional genomics oriented studies of model systems where time series measurements in the context of therapeutic interventions are becoming increasingly common. These studies will need computational models which accurately reflect the multi-factorial nature of allele measurement in cancer including, as we show here, segmental aneuploidies.