Publications by authors named "Antoine de Weck"

9 Publications

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

Nucleic Acids Res 2021 Jul 27. Epub 2021 Jul 27.

Institute of Medical Genetics and Pathology, University Hospital Basel, Basel, Switzerland.

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.
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http://dx.doi.org/10.1093/nar/gkab627DOI Listing
July 2021

Fully unsupervised deep mode of action learning for phenotyping high-content cellular images.

Bioinformatics 2021 Jul 8. Epub 2021 Jul 8.

Novartis Institutes for BioMedical Research Inc, Basel, Switzerland.

Motivation: The identification and discovery of phenotypes from high content screening (HCS) 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 labelled 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 unlabelled 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: Our code is available at https://github.com/Novartis/UMM-Discovery.

Supplementary Information: Supplementary data are available at Bioinformatics online.
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http://dx.doi.org/10.1093/bioinformatics/btab497DOI Listing
July 2021

ZNRF3 and RNF43 cooperate to safeguard metabolic liver zonation and hepatocyte proliferation.

Cell Stem Cell 2021 Jun 11. Epub 2021 Jun 11.

Novartis Institutes for BioMedical Research, Novartis Pharma AG, Basel, Switzerland. Electronic address:

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.
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http://dx.doi.org/10.1016/j.stem.2021.05.013DOI Listing
June 2021

DOT1L inhibition is lethal for multiple myeloma due to perturbation of the endoplasmic reticulum stress pathway.

Oncotarget 2020 Mar 17;11(11):956-968. Epub 2020 Mar 17.

Novartis Institutes for BioMedical Research (NIBR) Oncology, Basel, Switzerland.

The histone 3 lysine 79 (H3K79) methyltransferase (HMT) DOT1L is known to play a critical role for growth and survival of -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. 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.
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http://dx.doi.org/10.18632/oncotarget.27493DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7082114PMC
March 2020

Analysis of head and neck carcinoma progression reveals novel and relevant stage-specific changes associated with immortalisation and malignancy.

Sci Rep 2019 08 19;9(1):11992. Epub 2019 Aug 19.

Faculty of Biology, Medicine and Health, University of Manchester, Manchester, M13 9PL, UK.

We report changes in the genomic landscape in the development of head and neck squamous cell carcinomas HNSCC from potentially premalignant lesions (PPOLS) to malignancy and lymph node metastases. Likely pathological mutations predominantly involved a relatively small set of genes reported previously (TP53, KMT2D, CDKN2A, PIK3CA, NOTCH1 and FAT1) but also other predicted cancer drivers (MGA, PABPC3, NR4A2, NCOR1 and MACF1). Notably, all these mutations arise early and are present in PPOLs. The most frequent genetic changes, which follow acquisition of immortality and loss of senescence, are of consistent somatic copy number alterations (SCNAs) involving chromosomal regions enriched for genes in known and previously unreported cancer-related pathways. We mapped the evolution of SCNAs in HNSCC progression. One of the earliest SCNAs involved deletions of CSMD1 (8p23.2). CSMD1 deletions or promoter hypermethylation were present in all of the immortal PPOLs and occurred at high frequency in the immortal HNSCC cell lines. Modulation of CSMD1 in cell lines revealed significant suppression of proliferation and invasion by forced expression, and significant stimulation of invasion by knockdown of expression. Known cancer drivers NOTCH1, PPP6C, RAC1, EIF4G1, PIK3CA showed significant increase in frequency of SCNA in transition from PPOLs to HNSCC that correlated with their expression. In the later stages of progression, HNSCC with and without nodal metastases showed some clear differences including high copy number gains of CCND1, hsa-miR-548k and TP63 in the metastases group.
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http://dx.doi.org/10.1038/s41598-019-48229-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6700135PMC
August 2019

Next-generation characterization of the Cancer Cell Line Encyclopedia.

Nature 2019 05 8;569(7757):503-508. Epub 2019 May 8.

Broad Institute of Harvard and MIT, Cambridge, MA, USA.

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.
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http://dx.doi.org/10.1038/s41586-019-1186-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6697103PMC
May 2019

Correction of copy number induced false positives in CRISPR screens.

PLoS Comput Biol 2018 07 19;14(7):e1006279. Epub 2018 Jul 19.

Novartis Institutes for Biomedical Research, Basel, Switzerland.

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.
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http://dx.doi.org/10.1371/journal.pcbi.1006279DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6067744PMC
July 2018

Project DRIVE: A Compendium of Cancer Dependencies and Synthetic Lethal Relationships Uncovered by Large-Scale, Deep RNAi Screening.

Cell 2017 Jul;170(3):577-592.e10

Novartis Institutes for Biomedical Research, Oncology Disease Area, Basel 4002, Switzerland; Cambridge, MA 02139, USA; and Emeryville, CA 94608, USA.

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.
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http://dx.doi.org/10.1016/j.cell.2017.07.005DOI Listing
July 2017

Resistance mechanisms to TP53-MDM2 inhibition identified by in vivo piggyBac transposon mutagenesis screen in an Arf mouse model.

Proc Natl Acad Sci U S A 2017 03 6;114(12):3151-3156. Epub 2017 Mar 6.

Oncology Disease Area, Novartis Institutes for BioMedical Research, 4056 Basel, Switzerland.

Inhibitors of double minute 2 protein (MDM2)-tumor protein 53 (TP53) interaction are predicted to be effective in tumors in which the gene is wild type, by preventing TP53 protein degradation. One such setting is represented by the frequent deletion in human cancer that, through inactivation of , 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 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 () in 54% of tumors and transposon-mediated gain-of-function alterations in B-cell lymphoma-extra large (), , and two family members, resulting in expression of the TP53 dominant negative truncations and 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 wild-type tumors may escape MDM2-targeted therapy.
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http://dx.doi.org/10.1073/pnas.1620262114DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5373361PMC
March 2017
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