High-throughput RNA interference screening using pooled shRNA libraries and next generation sequencing

RNA interference (RNAi) screening is a state-of-the-art technology that enables the dissection of biological processes and disease-related phenotypes. The commercial availability of genome-wide, short hairpin RNA (shRNA) libraries has fueled interest in this area but the generation and analysis of these complex data remain a challenge. Here, we describe complete experimental protocols and novel open source computational methodologies, shALIGN and shRNAseq, that allow RNAi screens to be rapidly deconvoluted using next generation sequencing. Our computational pipeline offers efficient screen analysis and the flexibility and scalability to quickly incorporate future developments in shRNA library technology

Characterization of the genomic features and expressed fusion genes in micropapillary carcinomas of the breast

Micropapillary carcinoma ( MPC ) is a rare histological special type of breast cancer, characterized by an aggressive clinical behaviour and a pattern of copy number aberrations ( CNAs ) distinct from that of grade‐ and oestrogen receptor ( ER )‐matched invasive carcinomas of no special type ( IC‐NSTs ). The aims of this study were to determine whether MPCs are underpinned by a recurrent fusion gene(s) or mutations in 273 genes recurrently mutated in breast cancer. Sixteen MPCs were subjected to microarray‐based comparative genomic hybridization ( aCGH ) analysis and Sequenom OncoCarta mutation analysis. Eight and five MPCs were subjected to targeted capture and RNA sequencing, respectively. aCGH analysis confirmed our previous observations about the repertoire of CNAs of MPCs . Sequencing analysis revealed a spectrum of mutations similar to those of luminal B IC‐NSTs , and recurrent mutations affecting mitogen‐activated protein kinase family genes and NBPF10 . RNA ‐sequencing analysis identified 17 high‐confidence fusion genes, eight of which were validated and two of which were in‐frame. No recurrent fusions were identified in an independent series of MPCs and IC‐NSTs . Forced expression of in‐frame fusion genes ( SLC2A1–FAF1 and BCAS4–AURKA ) resulted in increased viability of breast cancer cells. In addition, genomic disruption of CDK12 caused by out‐of‐frame rearrangements was found in one MPC and in 13% of HER2 ‐positive breast cancers, identified through a re‐analysis of publicly available massively parallel sequencing data. In vitro analyses revealed that CDK12 gene disruption results in sensitivity to PARP inhibition, and forced expression of wild‐type CDK12 in a CDK12 ‐null cell line model resulted in relative resistance to PARP inhibition. Our findings demonstrate that MPCs are neither defined by highly recurrent mutations in the 273 genes tested, nor underpinned by a recurrent fusion gene. Although seemingly private genetic events, some of the fusion transcripts found in MPCs may play a role in maintenance of a malignant phenotype and potentially offer therapeutic opportunities. © 2014 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/106752/1/path4325.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/106752/2/path4325-sup-0001-AppendixS1.pd

Deriving a mutation index of carcinogenicity using protein structure and protein interfaces

With the advent of Next Generation Sequencing the identification of mutations in the genomes of healthy and diseased tissues has become commonplace. While much progress has been made to elucidate the aetiology of disease processes in cancer, the contributions to disease that many individual mutations make remain to be characterised and their downstream consequences on cancer phenotypes remain to be understood. Missense mutations commonly occur in cancers and their consequences remain challenging to predict. However, this knowledge is becoming more vital, for both assessing disease progression and for stratifying drug treatment regimes. Coupled with structural data, comprehensive genomic databases of mutations such as the 1000 Genomes project and COSMIC give an opportunity to investigate general principles of how cancer mutations disrupt proteins and their interactions at the molecular and network level. We describe a comprehensive comparison of cancer and neutral missense mutations; by combining features derived from structural and interface properties we have developed a carcinogenicity predictor, InCa (Index of Carcinogenicity). Upon comparison with other methods, we observe that InCa can predict mutations that might not be detected by other methods. We also discuss general limitations shared by all predictors that attempt to predict driver mutations and discuss how this could impact high-throughput predictions. A web interface to a server implementation is publicly available at http://inca.icr.ac.uk/

Comprehensive Genomic Analysis of a BRCA2 Deficient Human Pancreatic Cancer

Capan-1 is a well-characterised BRCA2-deficient human cell line isolated from a liver metastasis of a pancreatic adenocarcinoma. Here we report a genome-wide assessment of structural variations and high-depth exome characterization of single nucleotide variants and small insertion/deletions in Capan-1. To identify potential somatic and tumour-associated variations in the absence of a matched-normal cell line, we devised a novel method based on the analysis of HapMap samples. We demonstrate that Capan-1 has one of the most rearranged genomes sequenced to date. Furthermore, small insertions and deletions are detected more frequently in the context of short sequence repeats than in other genomes. We also identify a number of novel mutations that may represent genetic changes that have contributed to tumour progression. These data provide insight into the genomic effects of loss of BRCA2 function

Unique mutations and proteins predicted as drivers by InCa and by CHASM from mutations in COSMIC that were not in the training driver set.

<p>Unique mutations and proteins predicted as drivers by InCa and by CHASM from mutations in COSMIC that were not in the training driver set.</p

Summary of top mutation types in each category enriched in drivers.

<p>Summary of top mutation types in each category enriched in drivers.</p

ROC curve and prediction parameters for optimal thresholds in all tested methods.

<p>ROC curve and prediction parameters for optimal thresholds in all tested methods.</p

Contingency tables for post-translational modifications, disulphide bonds and electrostatic (h-bonds and salt bridges) interface bonds in mutated residues.

<p>P-values are calculated with a Fisher's test with a two-sided alternative hypothesis.</p