Biology subjects

Papenfuss, A. T.

Publications and source records attributed to Papenfuss, A. T..

At least 19 recordsLinked to original sources

bioRxiv2026

ICOS blockade alleviates immune suppression in breast tumor microenvironment

Immune checkpoint blockade has become a standard of care for triple-negative breast cancer, yet only around half of patients benefit. Compensatory immunoregulatory pathways may limit the efficacy of PD-1/PD-L1 blockade. ICOS, an inducible co-stimulatory receptor enriched on activated regulatory T cells (Tregs), represents a potential route of such immune regulation, but its role in breast cancer immunotherapy remains unclear. Here, we identify ICOS as selectively upregulated on tumor-infiltrating Tregs and show that ICOS Tregs localize within a tumor niche enriched for exhausted CD8 T cells. We confirmed this cellular and spatial association across 1,359 tumor regions from 279 triple-negative breast cancer patients profiled by imaging mass cytometry, and single-cell transcriptomic data from 129 breast cancer donors. Genetic ablation or pharmacological blockade of ICOS disrupted this intratumoral Treg niche, reducing Treg activation and tissue-residency programs while expanding cytotoxic CD8 T cells with a tissue-resident memory phenotype. In syngeneic orthotopic breast cancer models, ICOS blockade restricted primary tumor growth, reduced spontaneous metastasis and prolonged survival without disrupting systemic immune homeostasis. Therapeutic activity was observed with both neoadjuvant and adjuvant treatment, while combined ICOS and PD-1 blockade further enhanced tumor control. These findings support ICOS blockade as a therapeutic strategy to improve checkpoint immunotherapy.

cancer biology↗

bioRxiv2026

Genomic alterations enable BRCA1 methylation loss and promoter bypass to drive resistance in high-grade serous ovarian cancer

BRCA1 promoter methylation predicts sensitivity to PARP inhibitors in high-grade serous ovarian cancer, yet therapeutic resistance is common and mechanistically unresolved. Using long-read direct DNA sequencing of patient-derived xenografts and cell lines, we resolved BRCA1 methylation at single-molecule resolution with structural and transcriptomic analyses. We revealed two convergent PARP inhibitor and platinum resistance mechanisms, validated in patient tumors. First, focal, allele-specific loss of BRCA1 methylation arose through local cis-acting genomic alterations, instead of global epigenetic reprogramming. Engineered in cis sequence alterations near the methylated BRCA1 promoter were sufficient to induce methylation loss, restore homologous recombination, and confer resistance. Similar associations were observed across the genome, suggesting this mechanism extends beyond BRCA1. Second, BRCA1 expression was restored despite intact promoter methylation via structural variant-mediated promoter bypass or alternative transcription initiation. Together, these findings redefine BRCA1 methylation loss as a locus-restricted process and reveal multiple routes by which tumors escape PARP inhibitor therapy. Statement of SignificanceWe show that high-grade serous ovarian cancers can restore BRCA1 expression after therapy through multiple genomic mechanisms, including local methylation loss and promoter bypass, thereby re-establishing homologous recombination and driving PARP inhibitor resistance. These findings challenge reliance on BRCA1 methylation alone as a predictive biomarker and support rational combination therapies for more durable responses.

cancer biology↗

bioRxiv2026

cellNexus: Quality control, annotation, aggregation and analytical layers for the Human Cell Atlas data

Large-scale single-cell atlases such as the Human Cell Atlas have transformed our understanding of human biology. Yet, the lack of a robust framework that standardises quality control, expands cellular annotation, and adds normalisation and analytical layers, limits multi-study analyses and the usefulness of this resource. Here we present cellNexus, a comprehensive resource that enhances the Human Cell Atlas collection into analysis-ready data by linking quality control layers, metadata enrichment, expression normalisation, analysis and data aggregation. These enhancements enable robust large-scale statistical modelling across studies, exemplified here by a multi-tissue map of immune cell communication during ageing. All harmonised layers are accessible via a public web interface and with R and Python APIs. By providing continuous integration with CELLxGENE releases, cellNexus transforms large cell atlas corpora into an accessible, reproducible, interoperable foundation for large-scale biological discovery and the next generation of single-cell foundation models.

bioinformatics↗

bioRxiv2025

Improved marker detection for rare population in single-cell transcriptomics through text mining-inspired scoring approach

Accurate identification of cell types and states is a crucial step when analysing single cell RNA-seq data. Inaccurate cell type annotation leads to spurious results and biological interpretations in subsequent analyses. Cell type identification is traditionally done using established marker genes of each cell population. However, many existing methods do not perform well for imbalanced cell populations, which often occurs in the presence of rare cell types. Most existing methods tend to be biased towards the dominant cell populations or highly expressed genes, leading to inaccurate results. Here, we introduce the package smartid, which accurately identifies markers from even imbalanced data across batches by employing a modified Term Frequency-Inverse Document Frequency approach with Gaussian Mixture Model. smartid is also a gene-set scoring method which is able to distinguish the target group of interest. smartid is implemented in R and is freely available on Bioconductor at https://bioconductor.org/packages/release/bioc/html/smartid.html.

bioinformatics↗

bioRxiv2025

ProteinDJ: a high-performance and modular protein design pipeline

Leveraging artificial intelligence and deep learning to generate proteins de novo (a.k.a. synthetic proteins) has unlocked new frontiers of protein design. Deep learning models trained on protein structures can generate novel protein designs that explore structural landscapes unseen by evolution. This approach enables the development of bespoke binders that target specific proteins and domains through new protein-protein interactions. However, successful binder generation can suffer from low in silico success rates, often requiring thousands of designs and hundreds of GPU hours to obtain enough hits for experimental testing. While workstation implementations are available for binder design, these are limited in both scalability and throughput. There is a lack of efficient open-source protein design pipelines for high-performance computing (HPC) systems that can maximise hardware resources and parallelise the workflow efficiently. Here, we present ProteinDJ--an implementation of a synthetic protein design workflow that is deployable on HPC systems using the Nextflow portable workflow management system and Apptainer containerisation. It parallelises the workload across both GPUs and CPUs, facilitating generation and testing of hundreds of designs per hour, accelerating the discovery process. ProteinDJ is designed to be modular and includes RoseTTAFold Diffusion (RFdiffusion) or BindCraft for fold generation, ProteinMPNN or Full-Atom MPNN (FAMPNN) for sequence design, and AlphaFold2 or Boltz-2 for prediction and validation of designs and binder-target interfaces, with supporting packages for structural evaluation of designs. ProteinDJ democratises protein binder design through its robust and user-friendly implementation and provides a framework for future protein design pipelines. ProteinDJ is freely available at https://github.com/PapenfussLab/proteindj.

bioinformatics↗

bioRxiv2025

Deconvolution of the on-target activity of plasmepsin V peptidomimetics in P. falciparum parasites

Plasmepsin V (PMV), an essential aspartyl protease, plays a critical role during the asexual blood stage of infection of Plasmodium by enabling the export of parasite proteins into the host red blood cell. This export is vital for parasite survival and pathogenesis, making PMV an attractive target for antimalarial drug development. Peptidomimetic inhibitors designed to mimic the natural substrate of PMV have demonstrated potent parasite-killing activity by blocking protein export. While these compounds have been instrumental in validating PMV as a bona fide antimalarial target, inconsistencies between their biochemical potency and cellular activity have raised questions regarding their precise mechanism of action. In this study, we employed chemoproteomic approaches, including solvent induced protein precipitation (SIP) and intact-cell thermal PISA profiling, to demonstrate PMV target engagement by the peptidomimetics. To further support these findings, we generated parasite lines exhibiting reduced sensitivity to peptidomimetics. Through whole-genome sequencing of these parasite lines, a single nucleotide variant (SNV) within the pmv gene was revealed. This mutation was later validated using reverse genetics, confirming its role in mediating resistance. Together, these data provide strong evidence that the peptidomimetics exert their antimalarial activity by directly targeting PMV. These findings further support the potential of PMV as a validated and promising target for future antimalarial drug development.

cell biology↗

bioRxiv2024

The JNK and Hippo pathways control epithelial integrity and prevent tumour initiation by regulating an overlapping transcriptome

Epithelial organs maintain their integrity and prevent tumour initiation by actively removing defective cells, such as those that have lost apicobasal polarity. Here, we identify how transcription factors of two key signalling pathways - Jun-N-terminal kinase (JNK) and Hippo - regulate epithelial integrity by controlling transcription of an overlapping set of target genes. Targeted DamID experiments reveal that in proliferating cells of the Drosophila melanogaster eye, the AP-1 transcription factor Jun, and the Hippo pathway transcription regulators Yorkie and Scalloped bind to a common suite of target genes that regulate organ growth. In defective neoplastic cells, AP-1 transcription factors repress transcription of growth genes together with the CtBP co-repressor. If gene repression by AP-1/CtBP fails, neoplastic tumour growth ensues, driven by Yorkie/Scalloped. Thus, AP-1/CtBP eliminates defective cells and prevents tumour initiation by acting in parallel to Yorkie/Scalloped to repress expression of a shared transcriptome. These findings shed new light on the maintenance of epithelial integrity and tumour suppression.

developmental biology↗

bioRxiv2023

The tidyomics ecosystem: Enhancing omic data analyses

The growth of omic data presents evolving challenges in data manipulation, analysis, and integration. Addressing these challenges, Bioconductor1 provides an extensive community-driven biological data analysis platform. Meanwhile, tidy R programming2 offers a revolutionary standard for data organisation and manipulation. Here, we present the tidyomics software ecosystem, bridging Bioconductor to the tidy R paradigm. This ecosystem aims to streamline omic analysis, ease learning, and encourage cross-disciplinary collaborations. We demonstrate the effectiveness of tidyomics by analysing 7.5 million peripheral blood mononuclear cells from the Human Cell Atlas3, spanning six data frameworks and ten analysis tools.

bioinformatics↗

bioRxiv2023

A Risk-reward Examination of Sample Multiplexing Reagents for Single Cell RNA-Seq

Single-cell RNA sequencing (scRNA-Seq) has emerged as a powerful tool for understanding cellular heterogeneity and function. However the choice of sample multiplexing reagents can impact data quality and experimental outcomes. In this study, we compared various multiplexing reagents, including MULTI-Seq, Hashtag antibody, and CellPlex, across diverse sample types such as human peripheral blood mononuclear cells (PBMCs), mouse embryonic brain and patient-derived xenografts (PDXs). We found that all multiplexing reagents worked well in cell types robust to ex vivo manipulation but suffered from signal-to-noise issues in more delicate sample types. We compared multiple demultiplexing algorithms which differed in performance depending on data quality. We find that minor improvements to laboratory workflows such as titration and rapid processing are critical to optimal performance. We also compared the performance of fixed scRNA-Seq kits and highlight the advantages of the Parse Biosciences kit for fragile samples. Highly multiplexed scRNA-Seq experiments require more sequencing resources, therefore we evaluated CRISPR-based destruction of non-informative genes to enhance sequencing value. Our comprehensive analysis provides insights into the selection of appropriate sample multiplexing reagents and protocols for scRNASeq experiments, facilitating more accurate and cost-effective studies.

genomics↗

bioRxiv2023

Whole body cell map tracks tissue-specific immune cell accumulation and plasticity loss through ageing

Understanding tissue biologys heterogeneity is crucial for advancing precision medicine. Despite the centrality of the immune system in tissue homeostasis, a detailed and comprehensive map of immune cell distribution and interactions across human tissues and demographics remains elusive. To fill this gap, we harmonised data from 12,981 single-cell RNA sequencing samples and curated 29 million cells from 45 anatomical sites to create a comprehensive compositional and transcriptional healthy map of the healthy immune system. We used this resource and a novel multilevel modelling approach to track immune ageing and test differences across sex and ethnicity. We uncovered conserved and tissue-specific immune-ageing programs, resolved sex-dependent differential ageing and identified ethnic diversity in clinically critical immune checkpoints. This study provides a quantitative baseline of the immune system, facilitating advances in precision medicine. By sharing our immune map, we hope to catalyse further breakthroughs in cancer, infectious disease, immunology and precision medicine.

immunology↗

bioRxiv2023

Disruption of metazoan gene regulatory networks in cancer alters the balance of co-expression between genes of unicellular and multicellular origins

Metazoans inherited genes from unicellular ancestors that perform essential biological processes such as cell division, metabolism and protein translation. Functioning multicellularity requires careful control and coordination of these unicellular genes to maintain tissue integrity and homeostasis. Gene regulatory networks (GRNs) formed during metazoan evolution to regulate conserved biological processes are frequently altered in cancer, resulting in over-expression of unicellular genes. We propose an imbalance in co-expression of unicellular (UC) and multicellular (MC) genes is a driving force in cancer. To investigate, we combined gene co-expression analysis to infer changes to GRNs in cancer with protein sequence conservation data to distinguish genes with UC and MC origins. Co-expression networks created using RNA sequencing data from 31 tumour types and normal tissue samples were divided into modules enriched for UC genes, MC genes or a mix of both (Mixed UC-MC modules). The greatest differences between tumour and normal tissue co-expression networks occurred within Mixed UC-MC modules. In particular, MC and UC genes not commonly co-expressed in normal tissues formed distinct co-expression modules seen only in tumours. The degree of rewiring of genes within Mixed UC-MC modules increased with both tumour grade and stage. Mixed UC-MC modules were enriched for somatic mutations in cancer genes, particularly copy-number amplifications, suggesting an important driver of the rewiring observed in tumours are copy-number changes. Overall, our study shows the greatest changes to gene co-expression patterns during tumour progression occur between genes of MC and UC origins, implicating the breakdown and rewiring of metazoan gene regulatory networks in cancer development and progression. Author summaryMulticellular organism cells follow certain rules that control and coordinate their growth and behavior. This happens because gene regulatory networks formed during the evolution of multicellularity to control the activity of genes inherited from unicellular ancestors. Cancer cells disobey these rules, growing and dividing in a competitive fashion analogous to that of colonial unicellular organisms. Here, we test the hypothesis that breakdown of gene regulatory networks enforcing multicellularity drives cancer progression by investigating 31 tumour types. Based on sequence similarity, genes were categorized as having origins in either unicellular or multicellular species. We found that the balance of expression unicellular and multicellular genes changes dramatically in cancer. Genes expressed together in normal tissues stop being co-expressed in tumors, while unicellular and multicellular genes that would not normally be expressed together in normal tissues become highly co-expressed. This phenomenon is more pronounced in cancers at more advanced stages, and sometimes occurs in association with gain or loss of parts of certain chromosomes. Our work indicates disruption and rewiring of gene regulatory networks that evolved to enforce multicellularity drives cancer progression by upsetting the carefully coordinated balance in the activity of unicellular and multicellular genes.

bioinformatics↗

bioRxiv2022

Integrating deep mutational scanning and low-throughput mutagenesis data to predict the impact of amino acid variants

Evaluating the impact of amino acid variants has been a critical challenge for studying protein function and interpreting genomic data. High-throughput experimental methods like deep mutational scanning (DMS) can measure the effect of large numbers of variants in a target protein, but because DMS studies have not been performed on all proteins, researchers also model DMS data computationally to estimate variant impacts by predictors. In this study, we extended a linear regression-based predictor to explore whether incorporating data from alanine scanning (AS), a widely-used low-throughput mutagenesis method, would improve prediction results. To evaluate our model, we collected 146 AS datasets, mapping to 54 DMS datasets across 22 distinct proteins. We show that improved model performance depends on the compatibility of the DMS and AS assays, and the scale of improvement is closely related to the correlation between DMS and AS results.

bioinformatics↗

bioRxiv2022

RNA decay defines the therapeutic response to transcriptional perturbation in cancer

Therapeutic targeting of dysregulated transcriptional programs has arisen as a promising strategy for the treatment of leukaemias. The therapeutic response to small molecule inhibitors of Bromodomain-Containing Proteins (BRD), such as BRD2 and BRD4, P300/cAMP-response element binding protein (CBP) and Cyclin Dependent Kinases (CDKs), is generally attributed to the selective disruption of oncogenic gene expression networks driven by enhancers, super-enhancers (SEs) and lineage-specific transcription factors (TFs), including the c-MYC oncogene. Using technologies such as thiol (SH)-linked alkylation for the metabolic sequencing of RNA sequencing (SLAM-seq) to profile messenger RNA (mRNA) decay and production rates, we demonstrate that gene intrinsic properties largely govern the selectivity associated with transcriptional inhibition, where total mRNA response signatures are dominated with genes that have short transcript half-lives, including those regulated by SEs and oncogenic TFs. Further highlighting that gene sensitivities only occur in the context of short transcript half-lives, stabilisation of the c-MYC transcript through changes in the 3 UTR rendered it insensitive to transcriptional targeting. However, this was not sufficient to rescue c-MYC target gene transcription and anti-leukaemia effects following transcriptional inhibition. Importantly, long-lived mRNAs encoding essential genes that evade transcriptional targeting can be rendered sensitive via modulation of mRNA decay kinetics through inhibition of the RNA Binding Protein (RBP), ELAV Like RNA binding protein 1 (ELAVL1)/ Human Antigen R (HuR). Taken together, these data demonstrate that mRNA decay shapes the therapeutic response to transcriptional perturbation and can be modulated for novel therapeutic outcomes using transcriptional agents in leukaemia.

cancer biology↗

bioRxiv2022

Robust differential composition and variability analysis for multisample cell omics

Cell omics such as single-cell genomics, proteomics and microbiomics allow the characterisation of tissue and microbial community composition, which can be compared between conditions to identify biological drivers. This strategy has been critical to unveiling markers of disease progression such as cancer and pathogen infection. For cell omic data, no method for differential variability analysis exists, and methods for differential composition analysis only take a few fundamental data properties into account. Here we introduce sccomp, a generalised method for differential composition and variability analyses able to jointly model data count distribution, compositionality, group-specific variability and proportion mean-variability association, with awareness against outliers. Sccomp is an extensive analysis framework that allows realistic data simulation and cross-study knowledge transfer. Here, we demonstrate that mean-variability association is ubiquitous across technologies showing the inadequacy of the very popular Dirichlet-multinomial modelling and provide mandatory principles for differential variability analysis. We show that sccomp accurately fits experimental data, with a 50% incremental improvement over state-of-the-art algorithms. Using sccomp, we identified novel differential constraints and composition in the microenvironment of primary breast cancer. Significance statementDetermining the composition of cell populations is made possible by technologies like single-cell transcriptomics, CyTOF and microbiome sequencing. Such analyses are now widespread across fields (~800 publications/month, Scopus). However, existing methods for differential abundance do not model all data features, and cell-type/taxa specific differential variability is not yet possible. Increase in the variability of tissue composition and microbial communities is a well-known indicator of loss of homeostasis and disease. A suitable statistical method would enable new types of analyses to identify component-specific loss of homeostasis for the first time. This and other innovations are now possible through our discovery of the mean-variability association for compositional data. Based on this fundamental observation, we have developed a new statistical model, sccomp, that enables differential variability analysis for composition data, improved differential abundance analyses, with cross-sample information borrowing, outlier identification and exclusion, realistic data simulation, based on experimental datasets, cross-study knowledge transfer.

bioinformatics↗

bioRxiv2022

Genome-wide in vivo CRISPR screens identify GATOR1 as a TP53 induced tumour suppressor

Identifying tumor suppressor genes is predicted to inform on the development of novel strategies for cancer therapy. To identify new lymphoma driving processes that cooperate with oncogenic MYC (which is abnormally highly expressed in [~]70% of human cancers) we have used a genome-wide CRISPR knockout screen in E{micro}-Myc;Cas9 transgenic hematopoietic stem and progenitor cells in vivo. We discovered that loss of any of the GATOR1 complex components - NPRL3, DEPDC5, NPRL2 - significantly accelerated c-MYC-driven lymphoma development in mice. Low expression of the GATOR1 complex genes correlated with poor survival outcomes for human patients with high MYC- expressing cancers. Murine lymphomas lacking GATOR1 were highly sensitive to mTOR inhibitors as a single agent therapy, both in vitro and in vivo. These findings identify inhibition of mTORC1 as a potent tumor suppressive mechanism in c-MYC-driven lymphomagenesis and suggest a new avenue for therapeutic intervention in GATOR1-deficient lymphomas through mTOR inhibition. Key PointsO_LIIn vivo CRISPR/Cas9 whole genome knockout screens identified the GATOR1 complex as a potent suppressor of c-MYC-driven lymphomagenesis. C_LIO_LIGATOR1 deficiency in MYC-driven lymphomas confers sensitivity to mTOR inhibition, suggesting a novel therapeutic approach. C_LI

cancer biology↗

bioRxiv2022

Generation and characterisation of P. falciparum parasites with a G358S mutation in the PfATP4 Na+ pump and clinically relevant levels of resistance to some PfATP4 inhibitors

Small-molecule inhibitors of PfATP4, a Plasmodium falciparum protein that is believed to pump Na+ out of the parasite while importing H+, are on track to become much-needed new antimalarial drugs. The spiroindolone cipargamin is poised to become the first PfATP4 inhibitor to reach the field, having performed strongly in Phase 1 and 2 clinical trials. Previous attempts to generate cipargamin-resistant parasites in the laboratory have yielded parasites with reduced susceptibility to the drug; however, the highest 50% inhibitory concentration reported to date is 24 nM. Here, we show that P. falciparum parasites can acquire a clinically-significant level of resistance to cipargamin that enables them to withstand micromolar concentrations of the drug. Independent experiments to generate high-level cipargamin resistance using different protocols and strains led to the same change each time - a G358S mutation in PfATP4. Parasites with this mutation showed high-level resistance not only to cipargamin, but also to the dihydroisoquinolone (+)-SJ733. However, for certain other (less clinically advanced) PfATP4-associated compounds the G358S mutation in PfATP4 conferred only moderate resistance or no resistance. The G358S mutation in PfATP4 did not affect parasite susceptibility to antimalarials that do not target PfATP4. The G358S mutation in PfATP4, and the equivalent mutation in the Toxoplasma gondii ATP4 homologue (G419S), decreased the sensitivity of the Na+-ATPase activity of ATP4 to inhibition by cipargamin and (+)-SJ733, and decreased the sensitivity of parasites expressing these ATP4 mutations to disruption of parasite Na+ regulation by cipargamin- and (+)-SJ733. The G358S mutation in PfATP4 reduced the affinity of the protein for Na+ and was associated with an increase in the parasites resting cytosolic Na+ concentration; however, no significant defect in parasite growth rate was observed. Our findings suggest that codon 358 in pfatp4 should be monitored closely in the field as a molecular marker for cipargamin resistance, and that PfATP4 inhibitors in clinical development should be tested for their activity against PfATP4G358S parasites.

microbiology↗

bioRxiv2021

Removing unwanted variation from large-scale cancer RNA-sequencing data

The accurate identification and effective removal of unwanted variation are essential to derive meaningful biological results from RNA-seq data, especially when the data come from large and complex studies. We have used The Cancer Genome Atlas (TCGA) RNA-seq data to show that library size, batch effects, and tumor purity are major sources of unwanted variation across all TCGA RNA-seq datasets and that existing gold standard approaches to normalizations fail to remove this unwanted variation. Additionally, we illustrate how different sources of unwanted variation can compromise downstream analyses, including gene co-expression, association between gene expression and survival outcomes, and cancer subtype identifications. Here, we propose the use of a novel strategy, pseudo-replicates of pseudo-samples (PRPS), to deploy the Removing Unwanted Variation III (RUV-III) method to remove different sources of unwanted variation from large and complex gene expression studies. Our approach requires at least one roughly known biologically homogenous subclass of samples shared across sources of unwanted variation. To create PRPS, we first need to identify the sources of unwanted variation, which we will call batches in the data. Then the gene expression measurements of biologically homogeneous sets of samples are averaged within batches, and the results called pseudo-samples. Pseudo-samples with the same biology and different batches are then defined to be pseudo-replicates and used in RUV-III as replicates. The variation between pseudo-samples of a set pseudo-replicates is mainly unwanted variation. We illustrate the value of our approach by comparing it to the TCGA normalizations on several TCGA RNA-seq datasets. RUV-III with PRPS can be used for any large genomics project involving multiple labs, technicians, or platforms.

bioinformatics↗

bioRxiv2021

Elys deficiency constrains Kras-driven tumour burden by amplifying oncogenic stress

The nucleoporin ELYS, encoded by AHCTF1, is a large multifunctional protein with essential roles in nuclear pore assembly and mitosis. Using a zebrafish model of hepatocellular carcinoma, in which the expression of an inducible mutant kras transgene (krasG12V) drives hepatocyte-specific hyperplasia and liver enlargement, we show that reducing ahctf1 gene dosage by 50% markedly shrinks tumour burden, while non-hyperplastic tissues are unaffected. We demonstrate that ahctf1 heterozygosity impairs nuclear pore formation, mitotic spindle assembly and chromosome segregation, leading to DNA damage and activation of TP53-dependent and independent mechanisms of cell death and cell cycle arrest. This selective vulnerability of cancer cells to mild disruption of Elys function uncovers a novel synthetic lethal interaction between ahctf1 and kras mutations that could be exploited therapeutically. Heterozygous expression of both ahctf1 and ranbp2, or treatment of heterozygous ahctf1 larvae with the nucleocytoplasmic transport inhibitor, Selinexor, completely blocked krasG12V-driven hepatocyte hyperplasia, revealing promising avenues for combinatorial treatments.

cancer biology↗