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Motivation The identification and discovery of phenotypes from high content screening images is a challenging task. Earlier works use image analysis pipelines to extract biological features, supervised training methods or generate features with neural networks pretrained on non-cellular images. We introduce a novel unsupervised deep learning algorithm to cluster cellular images with similar Mode-of-Action (MOA) together using only the images’ pixel intensity values as input. It corrects for batch effect during training. Importantly, our method does not require the extraction of cell candidates and works from the entire images directly. Results The method achieves competitive results on the labeled subset of the BBBC021 dataset with an accuracy of 97.09% for correctly classifying the MOA by nearest neighbors matching. Importantly, we can train our approach on unannotated datasets. Therefore, our method can discover novel MOAs and annotate unlabeled compounds. The ability to train end-to-end on the full resolution images makes our method easy to apply and allows it to further distinguish treatments by their effect on proliferation. Availability and implementation Our code is available at https://github.com/Novartis/UMM-Discovery. Supplementary information Supplementary data are available at Bioinformatics online.

The histone 3 lysine 79 (H3K79) methyltransferase (HMT) DOT1L is known to play a critical role for growth and survival of MLL-rearranged leukemia. Serendipitous observations during high-throughput drug screens indicated that the use of DOT1L inhibitors might be expandable to multiple myeloma (MM). Through pharmacologic and genetic experiments, we could validate that DOT1L is essential for growth and viability of a subset of MM cell lines, in line with a recent report from another team. In vivo activity against established MM xenografts was observed with a novel DOT1L inhibitor. In order to understand the molecular mechanism of the dependency in MM, we examined gene expression changes upon DOT1L inhibition in sensitive and insensitive cell lines and discovered that genes belonging to the endoplasmic reticulum (ER) stress pathway and protein synthesis machinery were specifically suppressed in sensitive cells. Whole-genome CRISPR screens in the presence or absence of a DOT1L inhibitor revealed that concomitant targeting of the H3K4me3 methyltransferase SETD1B increases the effect of DOT1L inhibition. Our results provide a strong basis for further investigating DOT1L and SETD1B as targets in MM.

Droplet-based single-cell omics, including single-cell RNA sequencing (scRNAseq), single-cell CRISPR perturbations (e.g., CROP-seq), and single-cell protein and transcriptomic profiling (CITE-seq) hold great promise for comprehensive cell profiling and genetic screening at the single-cell resolution. However, these technologies suffer from substantial noise, among which ambient signals present in the cell suspension may be the predominant source. Current models to address this issue are highly technology-specific and relatively scRNAseq-centric. while a universal model to describe the noise across these technologies may reveal this common source, improving the denoising accuracy. To this end, we explicitly examined these unexpected signals in multiple datasets across droplet-based technologies, summarised a predictable pattern, and developed single-cell Ambient Remover (scAR) – a hypothesis-driven machine learning model to predict and remove ambient signals (including mRNA counts, protein counts, and sgRNA counts) at the molecular level. We benchmarked scAR on three technologies – single-cell CRISPR screens, CITE-seq, and scRNAseq along with the state-of-the-art single-technology-specific approaches. scAR showed high denoising accuracy for each type of dataset.

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.Availability and implementationOur code is available athttps://github.com/Novartis/TRAWLING

BackgroundSystematic perturbation screens provide comprehensive resources for the elucidation of cancer driver genes, including rarely mutated genes that are missed by approaches focused on frequently mutated genes and driver genes for which the basis for oncogenicity is non-genetic. 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.ResultsHere we developed APSiC (Analysis ofPerturbationScreens foridentifying novelCancer genes) that can identify genetic and non-genetic drivers even with a limited number of samples. Applying APSiC to the large-scale deep shRNA screen Project DRIVE, APSiC identified well-known, pan-cancer genetic drivers, novel putative genetic drivers known to be dysregulated in specific cancer types and the context dependency of mRNA-splicing between cancer types. Additionally, APSiC discovered a median of 28 and 35 putative non-genetic oncogenes and tumor suppressor genes, respectively, for individual cancer types, including genes involved in genome stability maintenance and cell cycle. We functionally demonstrated thatLRRC4B, a putative non-genetic tumor suppressor gene that has not previously been associated with carcinogenesis, suppresses proliferation by delaying cell cycle and modulates apoptosis in breast cancer.ConclusionWe 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.

AXIN2 and LGR5 mark intestinal stem cells (ISCs) that require WNT/β-Catenin signaling for constant homeostatic proliferation. In contrast, AXIN2/LGR5+ pericentral hepatocytes show low proliferation rates despite a WNT/β-Catenin activity gradient required for metabolic liver zonation. The mechanisms restricting proliferation in AXIN2+ hepatocytes and metabolic gene expression in AXIN2+ ISCs remained elusive. We now show that restricted chromatin accessibility in ISCs prevents the expression of β-Catenin-regulated metabolic enzymes, whereas fine-tuning of WNT/β-Catenin activity by ZNRF3 and RNF43 restricts proliferation in chromatin-permissive AXIN2+ hepatocytes, while preserving metabolic function. ZNRF3 deletion promotes hepatocyte proliferation, which in turn becomes limited by RNF43 upregulation. Concomitant deletion of RNF43 in ZNRF3 mutant mice results in metabolic reprogramming of periportal hepatocytes and induces clonal expansion in a subset of hepatocytes, ultimately promoting liver tumors. Together, ZNRF3 and RNF43 cooperate to safeguard liver homeostasis by spatially and temporally restricting WNT/β-Catenin activity, balancing metabolic function and hepatocyte proliferation.

Large panels of comprehensively characterized human cancer models, including the Cancer Cell Line Encyclopedia (CCLE), have provided a rigorous framework with which to study genetic variants, candidate targets, and small-molecule and biological therapeutics and to identify new marker-driven cancer dependencies. To improve our understanding of the molecular features that contribute to cancer phenotypes, including drug responses, here we have expanded the characterizations of cancer cell lines to include genetic, RNA splicing, DNA methylation, histone H3 modification, microRNA expression and reverse-phase protein array data for 1,072 cell lines from individuals of various lineages and ethnicities. Integration of these data with functional characterizations such as drug-sensitivity, short hairpin RNA knockdown and CRISPR-Cas9 knockout data reveals potential targets for cancer drugs and associated biomarkers. Together, this dataset and an accompanying public data portal provide a resource for the acceleration of cancer research using model cancer cell lines.

Head and neck squamous cell carcinoma (HNSCC) is a widely prevalent cancer globally with high mortality and morbidity. We report here changes in the genomic landscape in the development of HNSCC from potentially premalignant lesions (PPOLS) to malignancy and lymph node metastases. Frequent likely pathological mutations are restricted to a relatively small set of genes includingTP53, CDKN2A,FBXW7,FAT1,NOTCH1andKMT2D; these arise early in tumour progression and are present in PPOLs withNOTCH1mutations restricted to cell lines from lesions that subsequently progressed to HNSCC. The most frequent genetic changes are of consistent somatic copy number alterations (SCNA). The earliest SCNAs involved deletions ofCSMD1(8p23.2),FHIT(3p14.2) andCDKN2A(9p21.3) together with gains of chromosome 20.CSMD1deletions or promoter hypermethylation were present in all of the immortal PPOLs and occurred at high frequency in the immortal HNSCC cell lines (promoter hypermethylation ~63%, hemizygous deletions ~75%, homozygous deletions ~18%). Forced expression ofCSMD1in the HNSCC cell line H103 showed significant suppression of proliferation (p=0.0053) and invasioninvitro(p=5.98X10−5) supporting a role forCSMD1inactivation in early head and neck carcinogenesis. In addition, knockdown ofCSMD1in theCSMD1-expressing BICR16 cell line showed significant stimulation of invasionin vitro(p=1.82 x 10−5) but not cell proliferation (p=0.239). HNSCC with and without nodal metastases showed some clear differences including high copy number gains ofCCND1, hsa-miR-548k andTP63in the metastases group. GISTIC peak SCNA regions showed significant enrichment (adj PLay SummaryCancers affecting the head and neck region are relatively common. A large percentage of these are of one particular type; these are generally detected late and are associated with poor prognosis. Early detection and treatment dramatically improve survival and reduces the damage associated with the cancer and its treatment. Cancers arise and progress because of changes in the genetic material of the cells. This study focused on identifying such changes in these cancers particularly in the early stages of development, which are not fully known. Identification of these changes is important in developing new treatments as well as markers of behaviour of cancers and also the early or ‘premalignant’ lesions. We used a well-characterised panel of cell lines generated from premalignant lesions as well as cancers, to identify mutations in genes, and an increase or decrease in number of copies of genes. We mapped new and previously identified changes in these cancers to specific stages in the development of these cancers and their spread. We additionally report here for the first time, alterations inCSMD1gene in early premalignant lesions; we further show that this is likely to result in increased ability of the cells to spread and possibly, multiply faster as well.

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., Author summary Cancer vulnerabilities have been identified by systematically disrupting individual genes in cancer cells and observing the resulting effect on cell proliferation. In recent years, a new gene editing technique called CRISPR has made it easier and cheaper to disrupt genes by precisely and completely suppressing the function of individual genes by cutting through its DNA. However, an artefact of the approach yields false positives: using CRISPR to target genes in regions of the genome which are abnormally repeated, called copy number alterations (CNA), has been shown to kill the investigated cells irrespective of the true essentiality of the amplified genes. This artefact is a particular issue when studying tumours, since CNAs are common in cancer. Additionally cancer-specific genes are known to selectively drive amplification, making the ability to assess the essentiality of genes in these regions even more important. Here we describe and provide the code to computationally correct for this artefact and recover the true essentiality of CNA genes.

Inhibitors of double minute 2 protein (MDM2)-tumor protein 53 (TP53) interaction are predicted to be effective in tumors in which the TP53 gene is wild type, by preventing TP53 protein degradation. One such setting is represented by the frequent CDKN2A deletion in human cancer that, through inactivation of p14ARF, activates MDM2 protein, which in turn degrades TP53 tumor suppressor. Here we used piggyBac (PB) transposon insertional mutagenesis to anticipate resistance mechanisms occurring during treatment with the MDM2-TP53 inhibitor HDM201. Constitutive PB mutagenesis in Arf-/- mice provided a collection of spontaneous tumors with characterized insertional genetic landscapes. Tumors were allografted in large cohorts of mice to assess the pharmacologic effects of HDM201. Sixteen out of 21 allograft models were sensitive to HDM201 but ultimately relapsed under treatment. A comparison of tumors with acquired resistance to HDM201 and untreated tumors identified 87 genes that were differentially and significantly targeted by the PB transposon. Resistant tumors displayed a complex clonality pattern suggesting the emergence of several resistant subclones. Among the most frequent alterations conferring resistance, we observed somatic and insertional loss-of-function mutations in transformation-related protein 53 (Trp53) in 54% of tumors and transposon-mediated gain-of-function alterations in B-cell lymphoma-extra large (Bcl-xL), Mdm4, and two TP53 family members, resulting in expression of the TP53 dominant negative truncations ΔNTrp63 and ΔNTrp73. Enhanced BCL-xL and MDM4 protein expression was confirmed in resistant tumors, as well as in HDM201-resistant patient-derived tumor xenografts. Interestingly, concomitant inhibition of MDM2 and BCL-xL demonstrated significant synergy in p53 wild-type cell lines in vitro. Collectively, our findings identify several potential mechanisms by which TP53 wild-type tumors may escape MDM2-targeted therapy.

Elucidation of the mutational landscape of human cancer has progressed rapidly and been accompanied by the development of therapeutics targeting 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 identify vulnerabilities relevant to specific cancer subtypes, 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 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 recapitulating 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.

Introduction: It is important to understand the molecular mechanisms acting in potentially malignant epithelial lesions of the oral mucosa and determine whether the disorder will remain stable, regress or progress to squamous cell carcinoma (OSCC). Some may transform to invasive OSCC independent of developing dysplasia as an intermediate step. Understanding the mechanisms and identifying predictive biomarkers is critical for treatment decisions. Accurate DNA copy number alterations (CNAs) and loss of heterozygosity (LOH) estimates in cancer cells are now detectable using single nucleotide polymorphism (SNP) genotyping microarrays and can be combined with gene expression data. Discovering genomic structural aberrations and their relationship to the expression levels of the genes therein can provide a basis for a deeper understanding of the molecular mechanisms leading to dysplasia and cancer. The main goal of this work is to explore the influence of structural mutations on gene expression in cancer. To do so we are using a dataset of 32 head and neck samples. Immortal carcinoma and dysplasia information were obtained from cell lines, while the mortal carcinoma and dysplasia information were obtained from primary cultures. Methods: The genotypic data were obtained from Illumina 550K Bead arrays and the gene expressions from Affymetrix HGU-133A microarrays. The CNAs and LOHs were determined using a newly developed statistical framework, OncoSNP, which can process complex data derived from heterogeneous samples. The software uses both the log R ratio and the B allele frequency in a Bayesian framework to estimate the genomic aberrations. Results: Firstly the genomic pattern of CNA and LOH were studied, displaying strong structure and identifying a subgroup of immortal samples with high level of amplification across all chromosomes. Secondly the matching gene expressions were analyzed. Finally both genomic and gene expression datasets were combined and genes of high/low gene expression response to underlying copy number changes were searched for. As a result we produced 3 classes of genes that show different behaviour in their expression levels as a response to copy number changes. The 1st group is formed by those genes whose gene expression correlates significantly to the underlying CN changes, we use a Spearman correlation. The 2nd and 3rd gene lists were chosen such that they do not correlate to CN but whose gene expression show high/low coefficient of variation respectively. We then went on to perform pathway analysis to show that these gene sets belong to distinct pathways related to cancer. The non responding group is enriched for genes related to metabolism, while the responding group is enriched for genes belonging to cell signalling pathways. These findings will help to understand how relatively large and variable genomic changes through LOH and CNA can lead to similar disease phenotypes. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 38. doi:10.1158/1538-7445.AM2011-38