Transcriptomic heterogeneity in multifocal prostate cancer

International audienc

Digital Sorting of Pure Cell Populations Enables Unambiguous Genetic Analysis of Heterogeneous Formalin-Fixed Paraffin-Embedded Tumors by Next Generation Sequencing

Precision medicine in oncology requires an accurate characterization of a tumor molecular profile for patient stratification. Though targeted deep sequencing is an effective tool to detect the presence of somatic sequence variants, a significant number of patient specimens do not meet the requirements needed for routine clinical application. Analysis is hindered by contamination of normal cells and inherent tumor heterogeneity, compounded with challenges of dealing with minute amounts of tissue and DNA damages common in formalin-fixed paraffin-embedded (FFPE) specimens. Here we present an innovative workflow using DEPArray™ system, a microchip-based digital sorter to achieve 100%-pure, homogenous subpopulations of cells from FFPE samples. Cells are distinguished by fluorescently labeled antibodies and DNA content. The ability to address tumor heterogeneity enables unambiguous determination of true-positive sequence variants, loss-of-heterozygosity as well as copy number variants. The proposed strategy overcomes the inherent trade-offs made between sensitivity and specificity in detecting genetic variants from a mixed population, thus rescuing for analysis even the smaller clinical samples with low tumor cellularity

Challenges in identifying cancer genes by analysis of exome sequencing data

Massively parallel sequencing has permitted an unprecedented examination of the cancer exome, leading to predictions that all genes important to cancer will soon be identified by genetic analysis of tumours. To examine this potential, here we evaluate the ability of state-of-the-art sequence analysis methods to specifically recover known cancer genes. While some cancer genes are identified by analysis of recurrence, spatial clustering or predicted impact of somatic mutations, many remain undetected due to lack of power to discriminate driver mutations from the background mutational load (13–60% recall of cancer genes impacted by somatic single-nucleotide variants, depending on the method). Cancer genes not detected by mutation recurrence also tend to be missed by all types of exome analysis. Nonetheless, these genes are implicated by other experiments such as functional genetic screens and expression profiling. These challenges are only partially addressed by increasing sample size and will likely hold even as greater numbers of tumours are analysed