Biology subjects

Voest, E.

Publications and source records attributed to Voest, E..

5 recordsLinked to original sources

bioRxiv2024

The regulatory grammar of human promoters uncovered by MPRA-trained deep learning

One of the major challenges in genomics is to build computational models that accurately predict genome-wide gene expression from the sequences of regulatory elements. At the heart of gene regulation are promoters, yet their regulatory logic is still incompletely understood. Here, we report PARM, a cell-type specific deep learning model trained on specially designed massively parallel reporter assays that query human promoter sequences. PARM requires [~]1,000 times less computational power than state-of-the-art technology, and reliably predicts autonomous promoter activity throughout the genome from DNA sequence alone, in multiple cell types. PARM can even design purely synthetic strong promoters. We leveraged PARM to systematically identify binding sites of transcription factors (TFs) that are likely to contribute to the activity of each natural human promoter. We uncovered and experimentally confirmed striking positional preferences of TFs that differ between activating and repressive regulatory functions, as well as a complex grammar of motif-motif interactions. For example, many, but not all, TFs act as repressors when their binding motif is located near or just downstream of the transcription start site. Our approach lays the foundation towards a deep understanding of the regulation of human promoters by TFs. HighlightsO_LICausality-trained deep learning model PARM captures regulatory grammar of human promoters C_LIO_LIPARM is highly economical, both experimentally and computationally C_LIO_LITranscription factors have different preferred positions for their regulatory activity C_LIO_LIMany (but not all) transcription factors act as repressors when binding downstream of transcription start sites C_LI

genetics↗

bioRxiv2022

Base editing screens map mutations affecting IFNγ signalling in cancer

IFN{gamma}signalling underpins host responses to infection, inflammation and anti-tumour immunity. Mutations in the IFN{gamma}signalling pathway cause immunological disorders, haematological malignancies, and resistance to immune checkpoint blockade (ICB) in cancer, however the function of most clinically observed variants remain unknown. Here, we systematically investigate the genetic determinants of IFN{gamma}response in colorectal cancer cells using CRISPR-Cas9 screens and base editing mutagenesis. Deep mutagenesis of JAK1 with cytidine and adenine base editors, combined with pathway-wide screens, reveal loss-of-function and gain-of-function mutations with clinical precedence, including causal variants in haematological malignancies and mutations detected in patients refractory to ICB. We functionally validate variants of uncertain significance in primary tumour organoids, where engineering missense mutations in JAK1 enhanced or reduced sensitivity to autologous tumour-reactive T cells. By classifying > 300 missense variants altering IFN{gamma} pathway activity, we demonstrate the utility of base editing for mutagenesis at scale, and generate a resource to inform genetic diagnosis.

cancer biology↗

bioRxiv2019

Multiple Low Dose therapy: an effective strategy to treat EGFR inhibitor-resistant NSCLC tumours

Targeted cancer drugs often elicit powerful initial responses, but generally fail to deliver long-term benefit due to the emergence of resistant cells1,2. This is thought to be the consequence of strong selective pressure exerted on the cancer drug target by a Maximum Tolerated Dose (MTD) of a drug3-5. We hypothesized that partial inhibition of multiple components in the same oncogenic signalling pathway might add up to complete pathway inhibition, while at the same time decreasing the selective pressure on each individual component to acquire a resistance mutation. We report here testing of this Multiple Low Dose (MLD) model of drug administration in Epidermal Growth Factor Receptor (EGFR) mutant non-small cell lung cancer (NSCLC). We show that as little as 20% of the individual drug doses required for full inhibition of cell viability is sufficient to completely block MAPK signalling and proliferation when used in 3D (RAF+MEK+ERK inhibitors) or 4D (EGFR+RAF+MEK+ERK inhibitors) combinations. Importantly, EGFR mutant NSCLC cells treated with EGFR inhibitors at a high dose rapidly developed resistance in vitro, but the cells treated with 3D or 4D MLD therapy did not. Moreover, NSCLC cells that had gained resistance to high dose anti-EGFR therapies were still sensitive to MLD therapy. Using several animal models, including patient derived xenografts of NSCLC tumours that are resistant to EGFR inhibitors erlotinib and osimertinib6,7, we found durable responses to MLD therapy without associated toxicity. These data support the notion that partial inhibition of multiple components of cancer-activated signalling pathways is difficult to circumvent and suggest that MLD therapy could deliver clinical benefit. We propose that MLD strategy could be an effective treatment option for EGFR mutant NSCLC patients, especially those having acquired resistance to even third generation EGFR inhibitor therapy.

cancer biology↗

bioRxiv2019

Partner-independent fusion gene detection by multiplexed CRISPR/Cas9 enrichment and long-read Nanopore sequencing

Fusion genes are hallmarks of various cancer types and important determinants for diagnosis, prognosis and treatment possibilities. The promiscuity of fusion genes with respect to partner choice and exact breakpoint-positions restricts their detection in the diagnostic setting, even for known and recurrent fusion gene configurations. To accurately identify these gene fusions in an unbiased manner, we developed FUDGE: a FUsion gene Detection assay from Gene Enrichment. FUDGE couples target-selected and strand-specific CRISPR/Cas9 activity for enrichment and detection of fusion gene drivers (e.g. BRAF, EWSR1, KMT2A/MLL) - without prior knowledge of fusion partner or breakpoint-location - to long-read Nanopore sequencing. FUDGE encompasses a dedicated bioinformatics approach (NanoFG) to detect fusion genes from Nanopore sequencing data. Our strategy is flexible with respect to target choice and enables multiplexed enrichment for simultaneous analysis of several genes in multiple samples in a single sequencing run. We observe on average a 508 fold on-target enrichment and identify fusion breakpoints at nucleotide resolution - all within two days. We demonstrate that FUDGE effectively identifies fusion genes in cancer cell lines, tumor samples and on whole genome amplified DNA irrespective of partner gene or breakpoint-position in 100% of cases. Furthermore, we show that FUDGE is superior to routine diagnostic methods for fusion gene detection. In summary, we have developed a rapid and versatile fusion gene detection assay, providing an unparalleled opportunity for pan-cancer detection of fusion genes in routine diagnostics.

genomics↗

bioRxiv2018

Pan-cancer whole genome analyses of metastatic solid tumors

Metastatic cancer is one of the major causes of death and is associated with poor treatment efficiency. A better understanding of the characteristics of late stage cancer is required to help tailor personalised treatment, reduce overtreatment and improve outcomes. Here we describe the largest pan-cancer study of metastatic solid tumor genomes, including 2,520 whole genome-sequenced tumor-normal pairs, analyzed at a median depth of 106x and 38x respectively, and surveying over 70 million somatic variants. Metastatic lesions were found to be very diverse, with mutation characteristics reflecting those of the primary tumor types, although with high rates of whole genome duplication events (56%). Metastatic lesions are relatively homogeneous with the vast majority (96%) of driver mutations being clonal and up to 80% of tumor suppressor genes bi-allelically inactivated through different mutational mechanisms. For 62% of all patients, genetic variants that may be associated with outcome of approved or experimental therapies were detected. These actionable events were distributed across various mutation types underlining the importance of comprehensive genomic tumor profiling for cancer precision medicine.

cancer biology↗