N4-hydroxycytidine, the active compound of Molnupiravir, promotes SARS-CoV-2 mutagenesis and escape from a neutralizing nanobody.

N4-hydroxycytidine (NHC), the active compound of the drug Molnupiravir, is incorporated into SARS-CoV-2 RNA, causing false base pairing. The desired result is an "error catastrophe," but this bears the risk of mutated virus progeny. To address this experimentally, we propagated the initial SARS-CoV-2 strain in the presence of NHC. Deep sequencing revealed numerous NHC-induced mutations and host-cell-adapted virus variants. The presence of the neutralizing nanobody Re5D06 selected for immune escape mutations, in particular p.E484K and p.F490S, which are key mutations of the Beta/Gamma and Omicron-XBB strains, respectively. With NHC treatment, nanobody resistance occurred two passages earlier than without. Thus, within the limitations of this purely in vitro study, we conclude that the combined action of Molnupiravir and a spike-neutralizing antagonist leads to the rapid emergence of escape mutants. We propose caution use and supervision when using Molnupiravir, especially when patients are still at risk of spreading virus.

Machine learning models for predicting severe COVID-19 outcomes in hospitals.

The aim of this observational retrospective study is to improve early risk stratification of hospitalized Covid-19 patients by predicting in-hospital mortality, transfer to intensive care unit (ICU) and mechanical ventilation from electronic health record data of the first 24 h after admission. Our machine learning model predicts in-hospital mortality (AUC = 0.918), transfer to ICU (AUC = 0.821) and the need for mechanical ventilation (AUC = 0.654) from a few laboratory data of the first 24 h after admission. Models based on dichotomous features indicating whether a laboratory value exceeds or falls below a threshold perform nearly as good as models based on numerical features. We devise completely data-driven and interpretable machine-learning models for the prediction of in-hospital mortality, transfer to ICU and mechanical ventilation for hospitalized Covid-19 patients within 24 h after admission. Numerical values of. CRP and blood sugar and dichotomous indicators for increased partial thromboplastin time (PTT) and glutamic oxaloacetic transaminase (GOT) are amongst the best predictors.

Increased replication origin firing links replication stress to whole chromosomal instability in human cancer.

Chromosomal instability (CIN) is a hallmark of cancer and comprises structural CIN (S-CIN) and numerical or whole chromosomal CIN (W-CIN). Recent work indicated that replication stress (RS), known to contribute to S-CIN, also affects mitotic chromosome segregation, possibly explaining the common co-existence of S-CIN and W-CIN in human cancer. Here, we show that RS-induced increased origin firing is sufficient to trigger W-CIN in human cancer cells. We discovered that overexpression of origin firing genes, including GINS1 and CDC45, correlates with W-CIN in human cancer specimens and causes W-CIN in otherwise chromosomally stable human cells. Furthermore, modulation of the ATR-CDK1-RIF1 axis increases the number of firing origins and leads to W-CIN. Importantly, chromosome missegregation upon additional origin firing is mediated by increased mitotic microtubule growth rates, a mitotic defect prevalent in chromosomally unstable cancer cells. Thus, our study identifies increased replication origin firing as a cancer-relevant trigger for chromosomal instability.

External Validation of COVID-19 Risk Scores during Three Waves of Pandemic in a German Cohort-A Retrospective Study.

Several risk scores were developed during the COVID-19 pandemic to identify patients at risk for critical illness as a basic step to personalizing medicine even in pandemic circumstances. However, the generalizability of these scores with regard to different populations, clinical settings, healthcare systems, and new epidemiological circumstances is unknown. The aim of our study was to compare the predictive validity of qSOFA, CRB65, NEWS, COVID-GRAM, and 4C-Mortality score. In a monocentric retrospective cohort, consecutively hospitalized adults with COVID-19 from February 2020 to June 2021 were included; risk scores at admission were calculated. The area under the receiver operating characteristic curve and the area under the precision-recall curve were compared using DeLong's method and a bootstrapping approach. A total of 347 patients were included; 23.6% were admitted to the ICU, and 9.2% died in a hospital. NEWS and 4C-Score performed best for the outcomes ICU admission and in-hospital mortality. The easy-to-use bedside score NEWS has proven to identify patients at risk for critical illness, whereas the more complex COVID-19-specific scores 4C and COVID-GRAM were not superior. Decreasing mortality and ICU-admission rates affected the discriminatory ability of all scores. A further evaluation of risk assessment is needed in view of new and rapidly changing epidemiological evolution.

Generation of realistic synthetic data using Multimodal Neural Ordinary Differential Equations.

Individual organizations, such as hospitals, pharmaceutical companies, and health insurance providers, are currently limited in their ability to collect data that are fully representative of a disease population. This can, in turn, negatively impact the generalization ability of statistical models and scientific insights. However, sharing data across different organizations is highly restricted by legal regulations. While federated data access concepts exist, they are technically and organizationally difficult to realize. An alternative approach would be to exchange synthetic patient data instead. In this work, we introduce the Multimodal Neural Ordinary Differential Equations (MultiNODEs), a hybrid, multimodal AI approach, which allows for generating highly realistic synthetic patient trajectories on a continuous time scale, hence enabling smooth interpolation and extrapolation of clinical studies. Our proposed method can integrate both static and longitudinal data, and implicitly handles missing values. We demonstrate the capabilities of MultiNODEs by applying them to real patient-level data from two independent clinical studies and simulated epidemiological data of an infectious disease.

Stemness Correlates Inversely with MHC Class I Expression in Pediatric Small Round Blue Cell Tumors.

Recently, immunotherapeutic approaches have become a feasible option for a subset of pediatric cancer patients. Low MHC class I expression hampers the use of immunotherapies relying on antigen presentation. A well-established stemness score (mRNAsi) was determined using the bulk transcriptomes of 1134 pediatric small round blue cell tumors. Interestingly, MHC class I gene expression (HLA-A/-B/-C) was correlated negatively with mRNAsi throughout all diagnostic entities: neuroblastomas (NB) (n = 88, r = −0.41, p < 0.001), the Ewing's sarcoma family of tumors (ESFT) (n = 117, r = −0.46, p < 0.001), rhabdomyosarcomas (RMS) (n = 158, r = −0.5, p < 0.001), Wilms tumors (WT) (n = 224, r = −0.39, p < 0.001), and central nervous system-primitive neuroectodermal tumors CNS-PNET (r = −0.49, p < 0.001), with the exception of medulloblastoma (MB) (n = 76, r = −0.24, p = 0.06). The negative correlation of MHC class I and mRNAsi was independent of clinical features in NB, RMS, and WT. In NB and WT, increased MHC class I was correlated negatively with tumor stage. RMS patients with a high expression of MHC class I and abundant CD8 T cells showed a prolonged overall survival (n = 148, p = 0.004). Possibly, low MHC class I expression and stemness in pediatric tumors are remnants of prenatal tumorigenesis from multipotent precursor cells. Further studies are needed to assess the usefulness of stemness and MHC class I as predictive markers.

Distinct and Common Features of Numerical and Structural Chromosomal Instability across Different Cancer Types.

A large proportion of tumours is characterised by numerical or structural chromosomal instability (CIN), defined as an increased rate of gaining or losing whole chromosomes (W-CIN) or of accumulating structural aberrations (S-CIN). Both W-CIN and S-CIN are associated with tumourigenesis, cancer progression, treatment resistance and clinical outcome. Although W-CIN and S-CIN can co-occur, they are initiated by different molecular events. By analysing tumour genomic data from 33 cancer types, we show that the majority of tumours with high levels of W-CIN underwent whole genome doubling, whereas S-CIN levels are strongly associated with homologous recombination deficiency. Both CIN phenotypes are prognostic in several cancer types. Most drugs are less efficient in high-CIN cell lines, but we also report compounds and drugs which should be investigated as targets for W-CIN or S-CIN. By analysing associations between CIN and bio-molecular entities with pathway and gene expression levels, we complement gene signatures of CIN and report that the drug resistance gene CKS1B is strongly associated with S-CIN. Finally, we propose a potential copy number-dependent mechanism to activate the PI3K pathway in high-S-CIN tumours.

Loss of USP28 and SPINT2 expression promotes cancer cell survival after whole genome doubling.

BACKGROUND: Whole genome doubling is a frequent event during cancer evolution and shapes the cancer genome due to the occurrence of chromosomal instability. Yet, erroneously arising human tetraploid cells usually do not proliferate due to p53 activation that leads to CDKN1A expression, cell cycle arrest, senescence and/or apoptosis. METHODS: To uncover the barriers that block the proliferation of tetraploids, we performed a RNAi mediated genome-wide screen in a human colorectal cancer cell line (HCT116). RESULTS: We identified 140 genes whose depletion improved the survival of tetraploid cells and characterized in depth two of them: SPINT2 and USP28. We found that SPINT2 is a general regulator of CDKN1A transcription via histone acetylation. Using mass spectrometry and immunoprecipitation, we found that USP28 interacts with NuMA1 and affects centrosome clustering. Tetraploid cells accumulate DNA damage and loss of USP28 reduces checkpoint activation, thus facilitating their proliferation. CONCLUSIONS: Our results indicate three aspects that contribute to the survival of tetraploid cells: (i) increased mitogenic signaling and reduced expression of cell cycle inhibitors, (ii) the ability to establish functional bipolar spindles and (iii) reduced DNA damage signaling.

MFmap: A semi-supervised generative model matching cell lines to tumours and cancer subtypes.

Translating in vitro results from experiments with cancer cell lines to clinical applications requires the selection of appropriate cell line models. Here we present MFmap (model fidelity map), a machine learning model to simultaneously predict the cancer subtype of a cell line and its similarity to an individual tumour sample. The MFmap is a semi-supervised generative model, which compresses high dimensional gene expression, copy number variation and mutation data into cancer subtype informed low dimensional latent representations. The accuracy (test set F1 score >90%) of the MFmap subtype prediction is validated in ten different cancer datasets. We use breast cancer and glioblastoma cohorts as examples to show how subtype specific drug sensitivity can be translated to individual tumour samples. The low dimensional latent representations extracted by MFmap explain known and novel subtype specific features and enable the analysis of cell-state transformations between different subtypes. From a methodological perspective, we report that MFmap is a semi-supervised method which simultaneously achieves good generative and predictive performance and thus opens opportunities in other areas of computational biology.

Systems approaches identify the consequences of monosomy in somatic human cells.

Chromosome loss that results in monosomy is detrimental to viability, yet it is frequently observed in cancers. How cancers survive with monosomy is unknown. Using p53-deficient monosomic cell lines, we find that chromosome loss impairs proliferation and genomic stability. Transcriptome and proteome analysis demonstrates reduced expression of genes encoded on the monosomes, which is partially compensated in some cases. Monosomy also induces global changes in gene expression. Pathway enrichment analysis reveals that genes involved in ribosome biogenesis and translation are downregulated in all monosomic cells analyzed. Consistently, monosomies display defects in protein synthesis and ribosome assembly. We further show that monosomies are incompatible with p53 expression, likely due to defects in ribosome biogenesis. Accordingly, impaired ribosome biogenesis and p53 inactivation are associated with monosomy in cancer. Our systematic study of monosomy in human cells explains why monosomy is so detrimental and reveals the importance of p53 for monosomy occurrence in cancer.

SEEDS: data driven inference of structural model errors and unknown inputs for dynamic systems biology.

SUMMARY: Dynamic models formulated as ordinary differential equations can provide information about the mechanistic and causal interactions in biological systems to guide targeted interventions and to design further experiments. Inaccurate knowledge about the structure, functional form and parameters of interactions is a major obstacle to mechanistic modeling. A further challenge is the open nature of biological systems which receive unknown inputs from their environment. The R-package SEEDS implements two recently developed algorithms to infer structural model errors and unknown inputs from output measurements. This information can facilitate efficient model recalibration as well as experimental design in the case of misfits between the initial model and data. AVAILABILITY AND IMPLEMENTATION: For the R-package seeds, see the CRAN server https://cran.r-project.org/package=seeds.

The p53/p73 - p21CIP1 tumor suppressor axis guards against chromosomal instability by restraining CDK1 in human cancer cells.

Whole chromosome instability (W-CIN) is a hallmark of human cancer and contributes to the evolvement of aneuploidy. W-CIN can be induced by abnormally increased microtubule plus end assembly rates during mitosis leading to the generation of lagging chromosomes during anaphase as a major form of mitotic errors in human cancer cells. Here, we show that loss of the tumor suppressor genes TP53 and TP73 can trigger increased mitotic microtubule assembly rates, lagging chromosomes, and W-CIN. CDKN1A, encoding for the CDK inhibitor p21CIP1, represents a critical target gene of p53/p73. Loss of p21CIP1 unleashes CDK1 activity which causes W-CIN in otherwise chromosomally stable cancer cells. Consequently, induction of CDK1 is sufficient to induce abnormal microtubule assembly rates and W-CIN. Vice versa, partial inhibition of CDK1 activity in chromosomally unstable cancer cells corrects abnormal microtubule behavior and suppresses W-CIN. Thus, our study shows that the p53/p73 - p21CIP1 tumor suppressor axis, whose loss is associated with W-CIN in human cancer, safeguards against chromosome missegregation and aneuploidy by preventing abnormally increased CDK1 activity.

Germline ancestry influences the evolutionary disease course in lung adenocarcinomas.

Precision medicine relies on targeting specific somatic alterations present in a patient's tumor. However, the extent to which germline ancestry may influence the somatic burden of disease has received little attention. We estimated the genetic ancestry of non-small-cell lung cancer (NSCLC) patients and performed an in-depth analysis of the influence of genetic ancestry on the evolutionary disease course. Compared with European Americans (EA), African Americans (AA) with lung adenocarcinoma (LUAD) were found to be significantly younger and smoke significantly less. However, LUADs from AAs exhibited a significantly higher somatic mutation burden, with a more pronounced tobacco carcinogen footprint and increased frequencies of alterations affecting cancer genes. Conversely, no significant differences were observed between lung squamous cell carcinomas (LUSC) from EAs and AAs. Our results suggest germline ancestry influences the somatic evolution of LUAD but not LUSC.

Searching for Errors in Models of Complex Dynamic Systems.

Mathematical modeling is seen as a key step to understand, predict, and control the temporal dynamics of interacting systems in such diverse areas like physics, biology, medicine, and economics. However, for large and complex systems we usually have only partial knowledge about the network, the coupling functions, and the interactions with the environment governing the dynamic behavior. This incomplete knowledge induces structural model errors which can in turn be the cause of erroneous model predictions or misguided interpretations. Uncovering the location of such structural model errors in large networks can be a daunting task for a modeler. Here, we present a data driven method to search for structural model errors and to confine their position in large and complex dynamic networks. We introduce a coherence measure for pairs of network nodes, which indicates, how difficult it is to distinguish these nodes as sources of an error. By clustering network nodes into coherence groups and inferring the cluster inputs we can decide, which cluster is affected by an error. We demonstrate the utility of our method for the C. elegans neural network, for a signal transduction model for UV-B light induced morphogenesis and for synthetic examples.

Modelling and mathematical analysis of the M$_{2}$ receptor-dependent joint signalling and secondary messenger network in CHO cells.

The muscarinic M$_{2}$ receptor is a prominent member of the GPCR family and strongly involved in heart diseases. Recently published experimental work explored the cellular response to iperoxo-induced M$_{2}$ receptor stimulation in Chinese hamster ovary (CHO) cells. To better understand these responses, we modelled and analysed the muscarinic M$_{2}$ receptor-dependent signalling pathway combined with relevant secondary messenger molecules using mass action. In our literature-based joint signalling and secondary messenger model, all binding and phosphorylation events are explicitly taken into account in order to enable subsequent stoichiometric matrix analysis. We propose constraint flux sampling (CFS) as a method to characterize the expected shift of the steady state reaction flux distribution due to the known amount of cAMP production and PDE4 activation. CFS correctly predicts an experimentally observable influence on the cytoskeleton structure (marked by actin and tubulin) and in consequence a change of the optical density of cells. In a second step, we use CFS to simulate the effect of knock-out experiments within our biological system, and thus to rank the influence of individual molecules on the observed change of the optical cell density. In particular, we confirm the relevance of the protein RGS14, which is supported by current literature. A combination of CFS with Elementary Flux Mode analysis enabled us to determine the possible underlying mechanism. Our analysis suggests that mathematical tools developed for metabolic network analysis can also be applied to mixed secondary messenger and signalling models. This could be very helpful to perform model checking with little effort and to generate hypotheses for further research if parameters are not known.

A Bayesian approach to estimating hidden variables as well as missing and wrong molecular interactions in ordinary differential equation-based mathematical models.

Ordinary differential equations (ODEs) are a popular approach to quantitatively model molecular networks based on biological knowledge. However, such knowledge is typically restricted. Wrongly modelled biological mechanisms as well as relevant external influence factors that are not included into the model are likely to manifest in major discrepancies between model predictions and experimental data. Finding the exact reasons for such observed discrepancies can be quite challenging in practice. In order to address this issue, we suggest a Bayesian approach to estimate hidden influences in ODE-based models. The method can distinguish between exogenous and endogenous hidden influences. Thus, we can detect wrongly specified as well as missed molecular interactions in the model. We demonstrate the performance of our Bayesian dynamic elastic-net with several ordinary differential equation models from the literature, such as human JAK-STAT signalling, information processing at the erythropoietin receptor, isomerization of liquid α-Pinene, G protein cycling in yeast and UV-B triggered signalling in plants. Moreover, we investigate a set of commonly known network motifs and a gene-regulatory network. Altogether our method supports the modeller in an algorithmic manner to identify possible sources of errors in ODE-based models on the basis of experimental data.

Correction: The Subclonal Architecture of Metastatic Breast Cancer: Results from a Prospective Community-Based Rapid Autopsy Program "CASCADE".

[This corrects the article DOI: 10.1371/journal.pmed.1002204.].

The Subclonal Architecture of Metastatic Breast Cancer: Results from a Prospective Community-Based Rapid Autopsy Program "CASCADE".

BACKGROUND: Understanding the cancer genome is seen as a key step in improving outcomes for cancer patients. Genomic assays are emerging as a possible avenue to personalised medicine in breast cancer. However, evolution of the cancer genome during the natural history of breast cancer is largely unknown, as is the profile of disease at death. We sought to study in detail these aspects of advanced breast cancers that have resulted in lethal disease. METHODS AND FINDINGS: Three patients with oestrogen-receptor (ER)-positive, human epidermal growth factor receptor 2 (HER2)-negative breast cancer and one patient with triple negative breast cancer underwent rapid autopsy as part of an institutional prospective community-based rapid autopsy program (CASCADE). Cases represented a range of management problems in breast cancer, including late relapse after early stage disease, de novo metastatic disease, discordant disease response, and disease refractory to treatment. Between 5 and 12 metastatic sites were collected at autopsy together with available primary tumours and longitudinal metastatic biopsies taken during life. Samples underwent paired tumour-normal whole exome sequencing and single nucleotide polymorphism (SNP) arrays. Subclonal architectures were inferred by jointly analysing all samples from each patient. Mutations were validated using high depth amplicon sequencing. Between cases, there were significant differences in mutational burden, driver mutations, mutational processes, and copy number variation. Within each case, we found dramatic heterogeneity in subclonal structure from primary to metastatic disease and between metastatic sites, such that no single lesion captured the breadth of disease. Metastatic cross-seeding was found in each case, and treatment drove subclonal diversification. Subclones displayed parallel evolution of treatment resistance in some cases and apparent augmentation of key oncogenic drivers as an alternative resistance mechanism. We also observed the role of mutational processes in subclonal evolution. Limitations of this study include the potential for bias introduced by joint analysis of formalin-fixed archival specimens with fresh specimens and the difficulties in resolving subclones with whole exome sequencing. Other alterations that could define subclones such as structural variants or epigenetic modifications were not assessed. CONCLUSIONS: This study highlights various mechanisms that shape the genome of metastatic breast cancer and the value of studying advanced disease in detail. Treatment drives significant genomic heterogeneity in breast cancers which has implications for disease monitoring and treatment selection in the personalised medicine paradigm.

DNA replication stress mediates APOBEC3 family mutagenesis in breast cancer.

BACKGROUND: The APOBEC3 family of cytidine deaminases mutate the cancer genome in a range of cancer types. Although many studies have documented the downstream effects of APOBEC3 activity through next-generation sequencing, less is known about their upstream regulation. In this study, we sought to identify a molecular basis for APOBEC3 expression and activation. RESULTS: HER2 amplification and PTEN loss promote DNA replication stress and APOBEC3B activity in vitro and correlate with APOBEC3 mutagenesis in vivo. HER2-enriched breast carcinomas display evidence of elevated levels of replication stress-associated DNA damage in vivo. Chemical and cytotoxic induction of replication stress, through aphidicolin, gemcitabine, camptothecin or hydroxyurea exposure, activates transcription of APOBEC3B via an ATR/Chk1-dependent pathway in vitro. APOBEC3B activation can be attenuated through repression of oncogenic signalling, small molecule inhibition of receptor tyrosine kinase signalling and alleviation of replication stress through nucleoside supplementation. CONCLUSION: These data link oncogene, loss of tumour suppressor gene and drug-induced replication stress with APOBEC3B activity, providing new insights into how cytidine deaminase-induced mutagenesis might be activated in tumourigenesis and limited therapeutically.