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.

Replication stress links structural and numerical cancer chromosomal instability.

Cancer chromosomal instability (CIN) results in an increased rate of change of chromosome number and structure and generates intratumour heterogeneity. CIN is observed in most solid tumours and is associated with both poor prognosis and drug resistance. Understanding a mechanistic basis for CIN is therefore paramount. Here we find evidence for impaired replication fork progression and increased DNA replication stress in CIN(+) colorectal cancer (CRC) cells relative to CIN(-) CRC cells, with structural chromosome abnormalities precipitating chromosome missegregation in mitosis. We identify three new CIN-suppressor genes (PIGN (also known as MCD4), MEX3C (RKHD2) and ZNF516 (KIAA0222)) encoded on chromosome 18q that are subject to frequent copy number loss in CIN(+) CRC. Chromosome 18q loss was temporally associated with aneuploidy onset at the adenoma-carcinoma transition. CIN-suppressor gene silencing leads to DNA replication stress, structural chromosome abnormalities and chromosome missegregation. Supplementing cells with nucleosides, to alleviate replication-associated damage, reduces the frequency of chromosome segregation errors after CIN-suppressor gene silencing, and attenuates segregation errors and DNA damage in CIN(+) cells. These data implicate a central role for replication stress in the generation of structural and numerical CIN, which may inform new therapeutic approaches to limit intratumour heterogeneity.

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.

Mathematical Modelling of Cation Transport and Regulation in Yeast.

Mathematical modelling of ion transport is a strategy to understand the complex interplay between various ionic species and their transporters. Such models should provide new insights and suggest new interesting experiments. Two essential variables in models for ion transport and control are the membrane potential and the intracellular pH, which generates an additional layer of complexity absent from many other models of biochemical reaction pathways. The aim of this text is to introduce the reader to the basic principles and assumptions of modelling in this field. A simplified model of potassium transport will be used as an example and will be derived in a step by step manner. This forms the basis for understanding the advantages and limitations of more complex models. These are briefly reviewed at the end of this chapter.

Membrane Transport in Yeast, An Introduction.

Research on membrane transport has made continuous progress in the last decades and remains an active field of scientific investigation. In the case of yeast, most of the research has been conducted for the model organism Saccharomyces cerevisiae, but also the so-called non-conventional yeasts are being studied, especially because of their peculiarities and, in some cases, specific transport systems. This book is based on the experience of several experts summarizing the current knowledge about important substrate transport processes in yeast. Each chapter provides both a general overview of the main transport characteristics of a specific substrate or group of substrates and the unique details that only an expert working in the field is able to transmit to the reader.

Role of Saccharomyces cerevisiae Trk1 in stabilization of intracellular potassium content upon changes in external potassium levels.

Saccharomyces cerevisiae cells are able to grow at very different potassium concentrations adapting its intracellular cation levels to changes in the external milieu. Potassium homeostasis in wild type cells resuspended in media with low potassium is an example of non-perfect adaptation since the same intracellular concentration is not approached irrespective of the extracellular levels of the cation. By using yeasts lacking the Trk1,2 system or expressing different versions of the mutated main plasma membrane potassium transporter (Trk1), we show that Trk1 is not essential for adaptation to potassium changes but the dynamics of potassium loss is very different in the wild type and in trk1,2 mutant or in yeasts expressing Trk1 versions with highly impaired transport characteristics. We also show that the pattern here described can be also fulfilled by heterologous expression of NcHAK1, a potassium transporter not belonging to the TRK family. Hyperpolarization and cationic drugs sensitivity in mutants with defective transport capacity provide additional support to the hypothesis of connections between the activity of the Trk system and the plasma membrane H(+) ATPase (Pma1) in the adaptive process.

Expression of regulators of mitotic fidelity are associated with intercellular heterogeneity and chromosomal instability in primary breast cancer.

Regulators of transition through mitosis such as SURVIVIN and Aurora kinase A (AURKA) have been previously implicated in the initiation of chromosomal instability (CIN), a driver of intratumour heterogeneity. We investigate the relationship between protein expression of these genes and directly quantified CIN, and their prognostic utility in breast cancer. The expression of SURVIVIN and AURKA was determined by immunohistochemistry in a cohort of 426 patients with primary breast cancer. The association between protein expression and histopathological characteristics, clinical outcome and CIN status, as determined by centromeric FISH and defined by modal centromere deviation, was analysed. Significantly poorer clinical outcome was observed in patients with high AURKA expression levels. Expression of SURVIVIN was elevated in ER-negative relative to ER-positive breast cancer. Both AURKA and SURVIVIN increased expression were significantly associated with breast cancer grade. There was a significant association between increased CIN and both increased AURKA and SURVIVIN expression. AURKA gene amplification was also associated with increased CIN. To our knowledge this is the largest study assessing CIN status in parallel with the expression of the mitotic regulators AURKA and SURVIVIN. These data suggest that elevated expression of AURKA and SURVIVIN, together with AURKA gene amplification, are associated with increased CIN in breast cancer, and may be used as a proxy for CIN in breast cancer samples in the absence of more advanced molecular measurements.

LRIG1 regulates cadherin-dependent contact inhibition directing epithelial homeostasis and pre-invasive squamous cell carcinoma development.

Epidermal growth factor receptor (EGFR) pathway activation is a frequent event in human carcinomas. Mutations in EGFR itself are, however, rare, and the mechanisms regulating EGFR activation remain elusive. Leucine-rich immunoglobulin repeats-1 (LRIG1), an inhibitor of EGFR activity, is one of four genes identified that predict patient survival across solid tumour types including breast, lung, melanoma, glioma, and bladder. We show that deletion of Lrig1 is sufficient to promote murine airway hyperplasia through loss of contact inhibition and that re-expression of LRIG1 in human lung cancer cells inhibits tumourigenesis. LRIG1 regulation of contact inhibition occurs via ternary complex formation with EGFR and E-cadherin with downstream modulation of EGFR activity. We find that LRIG1 LOH is frequent across cancers and its loss is an early event in the development of human squamous carcinomas. Our findings imply that the early stages of squamous carcinoma development are driven by a change in amplitude of EGFR signalling governed by the loss of contact inhibition.

Profiling Numerical and Structural Chromosomal Instability in Different Cancer Types.

Many cancers display whole chromosome instability (W-CIN) and structural chromosomal instability (S-CIN), referring to increased rates of acquiring numerically and structurally abnormal chromosome changes. This protocol provides detailed steps to analyze the W-CIN and S-CIN across cancer types, intending to leverage large-scale bulk sequencing and SNP array data complemented with the computational models to gain a better understanding of W-CIN and S-CIN.

Potassium starvation in yeast: mechanisms of homeostasis revealed by mathematical modeling.

The intrinsic ability of cells to adapt to a wide range of environmental conditions is a fundamental process required for survival. Potassium is the most abundant cation in living cells and is required for essential cellular processes, including the regulation of cell volume, pH and protein synthesis. Yeast cells can grow from low micromolar to molar potassium concentrations and utilize sophisticated control mechanisms to keep the internal potassium concentration in a viable range. We developed a mathematical model for Saccharomyces cerevisiae to explore the complex interplay between biophysical forces and molecular regulation facilitating potassium homeostasis. By using a novel inference method ("the reverse tracking algorithm") we predicted and then verified experimentally that the main regulators under conditions of potassium starvation are proton fluxes responding to changes of potassium concentrations. In contrast to the prevailing view, we show that regulation of the main potassium transport systems (Trk1,2 and Nha1) in the plasma membrane is not sufficient to achieve homeostasis.

CERT depletion predicts chemotherapy benefit and mediates cytotoxic and polyploid-specific cancer cell death through autophagy induction.

Chromosomal instability (CIN) has been implicated in multidrug resistance and the silencing of the ceramide transporter, CERT, promotes sensitization to diverse cytotoxics. An improved understanding of mechanisms governing multidrug sensitization might provide insight into pathways contributing to the death of CIN cancer cells. Using an integrative functional genomics approach, we find that CERT-specific multidrug sensitization is associated with enhanced autophagosome-lysosome flux, resulting from the expression of LAMP2 following CERT silencing in colorectal and HER2(+) breast cancer cell lines. Live cell microscopy analysis revealed that CERT depletion induces LAMP2-dependent death of polyploid cells following exit from mitosis in the presence of paclitaxel. We find that CERT is relatively over-expressed in HER2(+) breast cancer and CERT protein expression acts as an independent prognostic variable and predictor of outcome in adjuvant chemotherapy-treated patients with primary breast cancer. These data suggest that the induction of LAMP2-dependent autophagic flux through CERT targeting may provide a rational approach to enhance multidrug sensitization and potentiate the death of polyploid cells following paclitaxel exposure to limit the acquisition of CIN and intra-tumour heterogeneity.

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.

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.

Chromosomal instability determines taxane response.

Microtubule-stabilizing (MTS) agents, such as taxanes, are important chemotherapeutics with a poorly understood mechanism of action. We identified a set of genes repressed in multiple cell lines in response to MTS agents and observed that these genes are overexpressed in tumors exhibiting chromosomal instability (CIN). Silencing 22/50 of these genes, many of which are involved in DNA repair, caused cancer cell death, suggesting that these genes are involved in the survival of aneuploid cells. Overexpression of these "CIN-survival" genes is associated with poor outcome in estrogen receptor-positive breast cancer and occurs frequently in basal-like and Her2-positive cases. In diploid cells, but not in chromosomally unstable cells, paclitaxel causes repression of CIN-survival genes, followed by cell death. In the OV01 ovarian cancer clinical trial, a high level of CIN was associated with taxane resistance but carboplatin sensitivity, indicating that CIN may determine MTS response in vivo. Thus, pretherapeutic assessment of CIN may optimize treatment stratification and clinical trial design using these agents.

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.

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.

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.

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.

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.