Systematic identification of novel cancer genes through analysis of deep shRNA perturbation screens.

Systematic perturbation screens provide comprehensive resources for the elucidation of cancer driver genes. The perturbation of many genes in relatively few cell lines in such functional screens necessitates the development of specialized computational tools with sufficient statistical power. Here we developed APSiC (Analysis of Perturbation Screens for identifying novel Cancer genes) to identify genetic drivers and effectors in perturbation screens even with few samples. Applying APSiC to the shRNA screen Project DRIVE, APSiC identified well-known and novel putative mutational and amplified cancer genes across all cancer types and in specific cancer types. Additionally, APSiC discovered tumor-promoting and tumor-suppressive effectors, respectively, for individual cancer types, including genes involved in cell cycle control, Wnt/β-catenin and hippo signalling pathways. We functionally demonstrated that LRRC4B, a putative novel tumor-suppressive effector, suppresses proliferation by delaying cell cycle and modulates apoptosis in breast cancer. We demonstrate APSiC is a robust statistical framework for discovery of novel cancer genes through analysis of large-scale perturbation screens. The analysis of DRIVE using APSiC is provided as a web portal and represents a valuable resource for the discovery of novel cancer genes

Correction of copy number induced false positives in CRISPR screens

<div>Data and scripts necessary to reproduce the results and figures presented in:<br></div><div><br></div><div>Correction of copy number induced false positives in CRISPR screens</div><div>Antoine de Weck, Javad Golji, Michael D. Jones, Joshua M. Korn, Eric Billy, E. Robert McDonald III, Tobias Schmelzle, Hans Bitter, Audrey Kauffmann</div><div>PLoS Computational Biology, 2018.</div><div><br></div

Project DRIVE - A compendium of cancer dependencies and synthetic lethal relationships uncovered by large scale, deep RNAi screening

The elucidation of the mutational landscape of human cancer has made rapid progress accompanied by the development of therapeutics targeting selected mutant oncogenes. However, a comprehensive mapping of cancer dependencies has lagged behind and the discovery of therapeutic targets for counteracting tumor suppressor gene loss is needed. To further the identification of vulnerabilities relevant to specific genetic and lineage subtypes of cancer we conducted a large-scale RNAi screen in which viability effects of mRNA knockdown were assessed for 7,837 genes using an average of 20 shRNAs per gene in 398 cancer cell lines. We describe the findings of this screen, outlining the classes of cancer dependency genes and their relationships to genetic, expression and lineage features. In addition, we describe robust gene-interaction networks that recapitulate both protein complexes and functional cooperation among complexes and pathways. This dataset along with a web portal is provided to the community to assist in the discovery and translation of new therapeutic approaches for cancer

TRAWLING: a Transcriptome Reference Aware of spLIciNG events

Alternative splicing is critical for human gene expression regulation and plays an important role in multiple human diseases. In this context, RNA sequencing has emerged as powerful approach to detect alternative splicing events. In parallel, fast alignment-free methods have emerged as a viable alternative to quantify gene and transcript level abundance from RNAseq data. However, the ability to detect differential splicing events is dependent on the annotation of the transcript reference provided by the user. Here, we introduce a new reference transcriptome aware of splicing events, TRAWLING, which simplifies the detection of aberrant splicing events in a fast and simple way. In addition, we evaluate the performances and the benefits of aligning transcriptome data to TRAWLING using three different RNA sequencing datasets: whole transcriptome sequencing, single cell RNA sequencing and Digital RNA with pertUrbation of Genes. Collectively, our comprehensive evaluation underlines the value of using TRAWLING in transcriptomic data analysis

High-content cellular screen image analysis benchmark study

Recent development of novel methods based on deep neural networks has transformed how high-content microscopy cellular images are analyzed. Nonetheless, it is still a challenge to identify cellular phenotypic changes caused by chemical or genetic treatments and to elucidate the relationships among treatments in an unsupervised manner, due to the large data volume, high phenotypic complexity and the presence of a priori unknown phenotypes. Here we benchmarked five deep neural network methods and two feature engineering methods on a well-characterized public data set. In contrast to previous benchmarking efforts, the manual annotations were not provided to the methods, but rather used as evaluation criteria afterwards. The seven methods individually performed feature extraction or representation learning from cellular images, and were consistently evaluated for downstream phenotype prediction and clustering tasks. We identified the strengths of individual methods across evaluation metrics, and further examined the biological concepts of features automatically learned by deep neural networks

High-content cellular screen image analysis benchmark study

Recent development of novel methods based on deep neural networks has transformed how high-content microscopy cellular images are analyzed. Nonetheless, it is still a challenge to identify cellular phenotypic changes caused by chemical or genetic treatments and to elucidate the relationships among treatments in an unsupervised manner, due to the large data volume, high phenotypic complexity and the presence of a priori unknown phenotypes. Here we benchmarked five deep neural network methods and two feature engineering methods on a well-characterized public data set. In contrast to previous benchmarking efforts, the manual annotations were not provided to the methods, but rather used as evaluation criteria afterwards. The seven methods individually performed feature extraction or representation learning from cellular images, and were consistently evaluated for downstream phenotype prediction and clustering tasks. We identified the strengths of individual methods across evaluation metrics, and further examined the biological concepts of features automatically learned by deep neural networks

Correction of copy number induced false positives in CRISPR screens

<div><p>Cell autonomous cancer dependencies are now routinely identified using CRISPR loss-of-function viability screens. However, a bias exists that makes it difficult to assess the true essentiality of genes located in amplicons, since the entire amplified region can exhibit lethal scores. These false-positive hits can either be discarded from further analysis, which in cancer models can represent a significant number of hits, or methods can be developed to rescue the true-positives within amplified regions. We propose two methods to rescue true positive hits in amplified regions by correcting for this copy number artefact. The Local Drop Out (LDO) method uses the relative lethality scores within genomic regions to assess true essentiality and does not require additional orthogonal data (e.g. copy number value). LDO is meant to be used in screens covering a dense region of the genome (e.g. a whole chromosome or the whole genome). The General Additive Model (GAM) method models the screening data as a function of the known copy number values and removes the systematic effect from the measured lethality. GAM does not require the same density as LDO, but does require prior knowledge of the copy number values. Both methods have been developed with single sample experiments in mind so that the correction can be applied even in smaller screens. Here we demonstrate the efficacy of both methods at removing the copy number effect and rescuing hits from some of the amplified regions. We estimate a 70–80% decrease of false positive hits with either method in regions of high copy number compared to no correction.</p></div

LDO removes the copy number effect across samples and maintains sensitivity of essential genes.

<p><b>A)</b> Boxplot of dependency scores across copy number for uncorrected and LDO corrected data. <b>B)</b> The recall curve for essential, nonessential and amplified genes is shown before and after LDO copy number correction in the cell line DAN-G. <b>C)</b> The area under the recall curve is shown across samples for the essential, nonessential and amplified genes.</p