Biology subjects

Wennerberg, K.

Publications and source records attributed to Wennerberg, K..

25 records · Page 2Linked to original sources

bioRxiv2020

Multi-modal meta-analysis of cancer cell line omics profiles identifies ECHDC1 as a novel breast tumor suppressor

A caveat of cancer cell line models is that their molecular and functional profiling is subject to laboratory-specific experimental practices and data analysis protocols. The current challenge is how to make an integrated use of omics profiles of cancer cell lines for reliable discoveries. Here, we carried out a systematic analysis of nine types of data modalities using meta-analysis of 53 omics profiling studies across 12 research laboratories for 2018 cell lines. To account for relatively low consistency observed for certain data modalities, we developed a robust data integration approach that identifies reproducible signals shared among multiple data modalities and studies. We demonstrated the power of the integrative analyses by identifying a novel driver gene, ECHDC1, with tumor suppressive role validated both in breast cancer cells and patient tumors. Extension of the approach identified synthetic lethal partners of cancer drivers, including a co-dependency of PTEN deficient cells on RNA helicases. HighlightsO_LIA comprehensive meta-analysis of 53 multi-modal omics profiles of >2000 cancer cell lines from 12 research laboratories C_LIO_LIAn unexpected lack of consistency between TMT-labelled and non-labelled global proteomic profiles C_LIO_LIA non-parametric approach to integrate omics profiles from multiple laboratories and to identify robust molecular patterns in individual cell lines C_LIO_LIThe multi-modal data integration reveals novel drivers and potential therapeutic targets, including ECHDC1 in breast cancers and DDX27 in PTEN mutant cancers. C_LI

cancer biology↗

bioRxiv2020

Crowdsourced mapping of unexplored target space of kinase inhibitors

Despite decades of intensive search for compounds that modulate the activity of particular targets, there are currently small-molecules available only for a small proportion of the human proteome. Effective approaches are therefore required to map the massive space of unexplored compound-target interactions for novel and potent activities. Here, we carried out a crowdsourced benchmarking of predictive models for kinase inhibitor potencies across multiple kinase families using unpublished bioactivity data. The top-performing predictions were based on kernel learning, gradient boosting and deep learning, and their ensemble resulted in predictive accuracy exceeding that of kinase activity assays. We then made new experiments based on the model predictions, which further improved the accuracy of experimental mapping efforts and identified unexpected potencies even for under-studied kinases. The open-source algorithms together with the novel bioactivities between 95 compounds and 295 kinases provide a resource for benchmarking new prediction algorithms and for extending the druggable kinome.

biochemistry↗

bioRxiv2019

Improved detection of differentially represented DNA barcodes for high-throughput lineage phenomics

Cellular DNA barcoding has become a popular approach to study heterogeneity of cell populations and to identify lineages with differential response to cellular stimuli. However, there is a lack of reliable methods for statistical inference of differentially responding lineages. Here, we used mixtures of DNA-barcoded cell pools to generate a realistic benchmark read count dataset for modelling a range of outcomes of lineage-tracing experiments. By accounting for the statistical properties intrinsic to the DNA barcode read count data, we implemented an improved algorithm that provides a significantly higher accuracy at detecting differentially responding lineages, compared to current RNA-seq data analysis algorithms. Building on the reliable statistical methodology, we illustrate how multidimensional phenotypic profiling (or high-throughput lineage phenomics) enables one to deconvolute phenotypically distinct cell subpopulations within a cancer cell line. The mixture control dataset and our analysis results provide a systematic foundation for benchmarking and improving algorithms for lineage-tracing experiments.

bioinformatics↗

bioRxiv2019

DNA barcode-guided lentiviral CRISPRa tool to trace and isolate individual clonal lineages in heterogeneous cancer cell populations

The genetic and functional heterogeneity of tumors imposes the challenge of understanding how a cancer progresses, evolves and adapts to treatment at the subclonal level. Therefore, there is a critical need for methods that enable profiling of individual cancer cell lineages. Here, we report a novel system that couples an established DNA barcoding technique for lineage tracing with a controlled DNA barcode-guided lineage isolation (B-GLI). B-GLI allows both high-complexity of lineage tracing and effective isolation of individual clones by CRISPRa-mediated induction of puromycin resistance, making it possible to unbiasedly trace, isolate, and study individual cancer cell lineages. We present experimental evaluation of the system performance in isolation of lineages and outline a comprehensive workflow for B-GLI applications. We believe the system has broad applications aimed at molecular and phenotypic profiling of individual lineages in heterogeneous cell populations.

cancer biology↗

bioRxiv2019

Epigenetic inhibitors sensitize DLBCL cells to rituximab and doxorubicin

Primary therapy for diffuse large B-cell lymphoma (DLBCL) is an immunochemotherapy regimen comprising rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP). While R-CHOP cures 60% of the patients with DLBCL, those who do not respond or relapse have dismal prognosis, and effective treatment strategies are needed. Due to the plastic nature of the epigenome and its fundamental role in regulating cell identity, we hypothesized that reprogramming the epigenome can overcome resistance to R-CHOP. We developed a novel drug screening protocol to identify epigenetic modifiers that sensitize DLBCL cell lines to immunochemotherapy. Of the herein tested 60 epigenetic compounds, we identified several histone deacetylase (HDAC) and histone methyltransferase (HMT) inhibitors that acted synergistically with immunochemotherapy. We show that sensitization through HDAC and HMT inhibitors is achieved by dysregulating homologous recombination, a central DNA repair pathway, as well as by disrupting the cell cycle and affecting the apoptotic pathway. Epigenetic inhibitors are well-tolerated, which together with our findings support their use in combination with immunochemotherapy in patients with primary refractory and relapsed DLBCL.

cancer biology↗

bioRxiv2018

Rules for PP2A-controlled phosphosignalling and drug responses

Systemic understanding of protein phosphatase 2A (PP2A)-regulated cellular processes is still at infancy. Here, we present mass-spectrometry analysis of phospho-targets (dephosphorylome) regulated by PP2A modulation. In addition to PP2A-regulated processes and targets, the data reveal important general concepts and rules related to PP2A-mediated phosphoregulation. These include the unidirectionality paradigm of regulation of phosphorylation, and differential spatial distribution of kinase-and phosphatase-dominated phosphotargets. Data also present first systemic analysis of targets of PP2A-modulating oncoproteins, CIP2A, PME-1, and SET; including targets via which PP2A may coordinately regulate activities of cancer drivers and tumor suppressors such as MYC or TP53. To validate functional utility of this dataset, PP2A dephosphorylome activity was correlated with cancer cell responses to over 300 drugs. Notably, we find that cancer therapy responses can be broadly classified based on PP2A dephosphorylome activity, both in quantitative and qualitative manner. In summary, our data characterize rules by which PP2A coordinate cancer cell phosphosignaling and drug responses. The results also may also direct the use of emerging pharmacological approaches for PP2A activity modulation in human diseases.

systems biology↗

bioRxiv2018

A normalized drug response metric improves accuracy and consistency of drug sensitivity quantification in cell-based screening

Accurate quantification of drug effects is crucial for identifying pharmaceutically actionable cancer vulnerabilities. Current cell viability-based measurements often lead to biased response estimates due to varying growth rates and experimental artifacts that explain part of the inconsistency in high-throughput screening results. We developed an improved drug scoring model, normalized drug response (NDR), which makes use of both positive and negative control conditions to account for differences in cell growth rates and experimental noise to better characterize drug-induced effects. We demonstrate an improved consistency and accuracy of NDR compared to existing metrics in assessing drug responses of cancer cells in various culture models and experimental setups. Notably, NDR reliably captures both toxicity and viability responses, and differentiates a wider spectrum of drug behavior, including lethal, growth-inhibitory and growth-stimulatory modes, based on a single viability readout. The method will therefore substantially reduce the time and resources required in cell-based drug sensitivity screening.

bioinformatics↗