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Digital twins represent a key technology for precision health. Medical digital twins consist of computational models that represent the health state of individual patients over time, enabling optimal therapeutics and forecasting patient prognosis. Many health conditions involve the immune system, so it is crucial to include its key features when designing medical digital twins. The immune response is complex and varies across diseases and patients, and its modelling requires the collective expertise of the clinical, immunology, and computational modelling communities. This review outlines the initial progress on immune digital twins and the various initiatives to facilitate communication between interdisciplinary communities. We also outline the crucial aspects of an immune digital twin design and the prerequisites for its implementation in the clinic. We propose some initial use cases that could serve as "proof of concept" regarding the utility of immune digital technology, focusing on diseases with a very different immune response across spatial and temporal scales (minutes, days, months, years). Lastly, we discuss the use of digital twins in drug discovery and point out emerging challenges that the scientific community needs to collectively overcome to make immune digital twins a reality., Competing Interests: Competing interests: The authors declare no competing interests., (© 2024. The Author(s).)

Background and Purpose: In Norway, comprehensive molecular tumour profiling is implemented as part of the public healthcare system. A substantial number of tumours harbour potentially targetable molecular alterations. Therapy outcomes may improve if targeted treatments are matched with actionable genomic alterations. In the IMPRESS-Norway trial (NCT04817956), patients are treated with drugs outside the labelled indication based on their tumours molecular profile., Patients and Methods: IMPRESS-Norway is a national, prospective, non-randomised, precision cancer medicine trial, offering treatment to patients with advanced-stage disease, progressing on standard treatment. Comprehensive next-generation sequencing, TruSight Oncology 500, is used for screening. Patients with tumours harbouring molecular alterations with matched targeted therapies available in IMPRESS-Norway, are offered treatment. Currently, 24 drugs are available in the study. Primary study endpoints are percentage of patients offered treatment in the trial, and disease control rate (DCR) defined as complete or partial response or stable disease in evaluable patients at 16 weeks (W16) of treatment. Secondary endpoint presented is DCR in all treated patients., Results: Between April 2021 and October 2023, 1,167 patients were screened, and an actionable mutation with matching drug was identified for 358 patients. By the data cut off 186 patients have initiated treatment, 170 had a minimum follow-up time of 16 weeks, and 145 also had evaluable disease. In patients with evaluable disease, the DCR was 40% (58/145). Secondary endpoint analysis of DCR in all treated patients, showed DCR of 34% (58/170)., Interpretation: Precision cancer medicine demonstrates encouraging clinical effect in a subset of patients included in the IMPRESS-Norway trial.

While chemotherapy alone or in combination with radiotherapy and surgery are important modalities in the treatment of colorectal cancer, their widespread use is not paired with an abundance of diagnostic tools to match individual patients with the most effective standard-of-care chemo- or radiotherapy regimens. Patient-derived organoids are tumour-derived structures that have been shown to retain certain aspects of the tissue of origin. We present here a systematic review of studies that have tested the performance of patient derived organoids to predict the effect of anti-cancer therapies in colorectal cancer, for chemotherapies, targeted drugs, and radiation therapy, and we found overall a positive predictive value of 68% and a negative predictive value of 78% for organoid informed treatment, which outperforms response rates observed with empirically guided treatment selection., (© 2023. Springer Nature Limited.)

Colorectal cancer (CRC) is one of the most prevalent cancers, driven by several factors including deregulations in intracellular signalling pathways. Small extracellular vesicles (sEVs) are nanosized protein-packaged particles released from cells, which are present in liquid biopsies. Here, we characterised the proteome landscape of sEVs and their cells of origin in three CRC cell lines HCT116, HT29 and SW620 to explore molecular traits that could be exploited as cancer biomarker candidates and how intracellular signalling can be assessed by sEV analysis instead of directly obtaining the cell of origin itself. Our findings revealed that sEV cargo clearly reflects its cell of origin with proteins of the PI3K-AKT pathway highly represented in sEVs. Proteins known to be involved in CRC were detected in both cells and sEVs including KRAS, ARAF, mTOR, PDPK1 and MAPK1, while TGFB1 and TGFBR2, known to be key players in epithelial cancer carcinogenesis, were found to be enriched in sEVs. Furthermore, the phosphopeptide-enriched profiling of cell lysates demonstrated a distinct pattern between cell lines and highlighted potential phosphoproteomic targets to be investigated in sEVs. The total proteomic and phosphoproteomics profiles described in the current work can serve as a source to identify candidates for cancer biomarkers that can potentially be assessed from liquid biopsies.

Background: Matching treatment based on tumour molecular characteristics has revolutionized the treatment of some cancers and has given hope to many patients. Although personalized cancer care is an old concept, renewed attention has arisen due to recent advancements in cancer diagnostics including access to high-throughput sequencing of tumour tissue. Targeted therapies interfering with cancer specific pathways have been developed and approved for subgroups of patients. These drugs might just as well be efficient in other diagnostic subgroups, not investigated in pharma-led clinical studies, but their potential use on new indications is never explored due to limited number of patients., Methods: In this national, investigator-initiated, prospective, open-label, non-randomized combined basket- and umbrella-trial, patients are enrolled in multiple parallel cohorts. Each cohort is defined by the patient's tumour type, molecular profile of the tumour, and study drug. Treatment outcome in each cohort is monitored by using a Simon two-stage-like 'admissible' monitoring plan to identify evidence of clinical activity. All drugs available in IMPRESS-Norway have regulatory approval and are funded by pharmaceutical companies. Molecular diagnostics are funded by the public health care system., Discussion: Precision oncology means to stratify treatment based on specific patient characteristics and the molecular profile of the tumor. Use of targeted drugs is currently restricted to specific biomarker-defined subgroups of patients according to their market authorization. However, other cancer patients might also benefit of treatment with these drugs if the same biomarker is present. The emerging technologies in molecular diagnostics are now being implemented in Norway and it is publicly reimbursed, thus more cancer patients will have a more comprehensive genomic profiling of their tumour. Patients with actionable genomic alterations in their tumour may have the possibility to try precision cancer drugs through IMPRESS-Norway, if standard treatment is no longer an option, and the drugs are available in the study. This might benefit some patients. In addition, it is a good example of a public-private collaboration to establish a national infrastructure for precision oncology. Trial registrations EudraCT: 2020-004414-35, registered 02/19/2021; ClinicalTrial.gov: NCT04817956, registered 03/26/2021., (© 2022. The Author(s).)

Causal molecular interactions represent key building blocks used in computational modeling, where they facilitate the assembly of regulatory networks. Logical regulatory networks can be used to predict biological and cellular behaviors by system perturbations and in silico simulations. Today, broad sets of causal interactions are available in a variety of biological knowledge resources. However, different visions, based on distinct biological interests, have led to the development of multiple ways to describe and annotate causal molecular interactions. It can therefore be challenging to efficiently explore various resources of causal interaction and maintain an overview of recorded contextual information that ensures valid use of the data. This review lists the different types of public resources with causal interactions, the different views on biological processes that they represent, the various data formats they use for data representation and storage, and the data exchange and conversion procedures that are available to extract and download these interactions. This may further raise awareness among the targeted audience, i.e. logical modelers and other scientists interested in molecular causal interactions, but also database managers and curators, about the abundance and variety of causal molecular interaction data, and the variety of tools and approaches to convert them into one interoperable resource., (© The Author(s) 2020. Published by Oxford University Press.)

Motivation: A large variety of molecular interactions occurs between biomolecular components in cells. When a molecular interaction results in a regulatory effect, exerted by one component onto a downstream component, a so-called 'causal interaction' takes place. Causal interactions constitute the building blocks in our understanding of larger regulatory networks in cells. These causal interactions and the biological processes they enable (e.g. gene regulation) need to be described with a careful appreciation of the underlying molecular reactions. A proper description of this information enables archiving, sharing and reuse by humans and for automated computational processing. Various representations of causal relationships between biological components are currently used in a variety of resources., Results: Here, we propose a checklist that accommodates current representations, called the Minimum Information about a Molecular Interaction CAusal STatement (MI2CAST). This checklist defines both the required core information, as well as a comprehensive set of other contextual details valuable to the end user and relevant for reusing and reproducing causal molecular interaction information. The MI2CAST checklist can be used as reporting guidelines when annotating and curating causal statements, while fostering uniformity and interoperability of the data across resources., Availability and Implementation: The checklist together with examples is accessible at https://github.com/MI2CAST/MI2CAST., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2020. Published by Oxford University Press.)

Cancer is a heterogeneous and complex disease and one of the leading causes of death worldwide. The high tumor heterogeneity between individuals affected by the same cancer type is accompanied by distinct molecular and phenotypic tumor profiles and variation in drug treatment response. In silico modeling of cancer as an aberrantly regulated system of interacting signaling molecules provides a basis to enhance our biological understanding of disease progression, and it offers the means to use computer simulations to test and optimize drug therapy designs on particular cancer types and subtypes. This sets the stage for precision medicine: the design of treatments tailored to individuals or groups of patients based on their tumor-specific molecular cancer profiles. Here, we show how a relatively large manually curated logical model can be efficiently enhanced further by including components highlighted by a multi-omics data analysis of data from Consensus Molecular Subtypes covering colorectal cancer. The model expansion was performed in a pathway-centric manner, following a partitioning of the model into functional subsystems, named modules. The resulting approach constitutes a middle-out modeling strategy enabling a data-driven expansion of a model from a generic and intermediate level of molecular detail to a model better covering relevant processes that are affected in specific cancer subtypes, comprising 183 biological entities and 603 interactions between them, partitioned in 25 functional modules of varying size and structure. We tested this model for its ability to correctly predict drug combination synergies, against a dataset of experimentally determined cell growth responses with 18 drugs in all combinations, on eight cancer cell lines. The results indicate that the extended model had an improved accuracy for drug synergy prediction for the majority of the experimentally tested cancer cell lines, although significant improvements of the model's predictive performance are still needed. Our study demonstrates how a tumor-data driven middle-out approach toward refining a logical model of a biological system can further customize a computer model to represent specific cancer cell lines and provide a basis for identifying synergistic effects of drugs targeting specific regulatory proteins. This approach bridges between preclinical cancer model data and clinical patient data and may thereby ultimately be of help to develop patient-specific in silico models that can steer treatment decisions in the clinic., (Copyright © 2020 Tsirvouli, Touré, Niederdorfer, Vázquez, Flobak and Kuiper.)

Discrete dynamical modeling shows promise in prioritizing drug combinations for screening efforts by reducing the experimental workload inherent to the vast numbers of possible drug combinations. We have investigated approaches to predict combination responses across different cancer cell lines using logic models generated from one generic prior-knowledge network representing 144 nodes covering major cancer signaling pathways. Cell-line specific models were configured to agree with baseline activity data from each unperturbed cell line. Testing against experimental data demonstrated a high number of true positive and true negative predictions, including also cell-specific responses. We demonstrate the possible enhancement of predictive capability of models by curation of literature knowledge further detailing subtle biologically founded signaling mechanisms in the model topology. In silico model analysis pinpointed a subset of network nodes highly influencing model predictions. Our results indicate that the performance of logic models can be improved by focusing on high-influence node protein activity data for model configuration and that these nodes accommodate high information flow in the regulatory network., (Copyright © 2020 Niederdorfer, Touré, Vazquez, Thommesen, Kuiper, Lægreid and Flobak.)

Drug combinations have been proposed to combat drug resistance, but putative treatments are challenged by low bench-to-bed translational efficiency. To explore the effect of cell culture format and readout methods on identification of synergistic drug combinations in vitro, we studied response to 21 clinically relevant drug combinations in standard planar (2D) layouts and physiologically more relevant spheroid (3D) cultures of HCT-116, HT-29 and SW-620 cells. By assessing changes in viability, confluency and spheroid size, we were able to identify readout- and culture format-independent synergies, as well as synergies specific to either culture format or readout method. In particular, we found that spheroids, compared to 2D cultures, were generally both more sensitive and showed greater synergistic response to combinations involving a MEK inhibitor. These results further shed light on the importance of including more complex culture models in order to increase the efficiency of drug discovery pipelines.

While there is a high interest in drug combinations in cancer therapy, openly accessible datasets for drug combination responses are sparse. Here we present a dataset comprising 171 pairwise combinations of 19 individual drugs targeting signal transduction mechanisms across eight cancer cell lines, where the effect of each drug and drug combination is reported as cell viability assessed by metabolic activity. Drugs are chosen by their capacity to specifically interfere with well-known signal transduction mechanisms. Signalling processes targeted by the drugs include PI3K/AKT, NFkB, JAK/STAT, CTNNB1/TCF, and MAPK pathways. Drug combinations are classified as synergistic based on the Bliss independence synergy metrics. The data identifies combinations that synergistically reduce cancer cell viability and that can be of interest for further pre-clinical investigations.

Motivation: Drug synergies are sought to identify combinations of drugs particularly beneficial. User-friendly software solutions that can assist analysis of large-scale datasets are required., Results: CImbinator is a web-service that can aid in batch-wise and in-depth analyzes of data from small-scale and large-scale drug combination screens. CImbinator offers to quantify drug combination effects, using both the commonly employed median effect equation, as well as advanced experimental mathematical models describing dose response relationships., Availability and Implementation: CImbinator is written in Ruby and R. It uses the R package drc for advanced drug response modeling. CImbinator is available at http://cimbinator.bioinfo.cnio.es , the source-code is open and available at https://github.com/Rbbt-Workflows/combination_index . A Docker image is also available at https://hub.docker.com/r/mikisvaz/rbbt-ci_mbinator/ ., Contact: asmund.flobak@ntnu.no or miguel.vazquez@cnio.es., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2017. Published by Oxford University Press.)

Discovery of efficient anti-cancer drug combinations is a major challenge, since experimental testing of all possible combinations is clearly impossible. Recent efforts to computationally predict drug combination responses retain this experimental search space, as model definitions typically rely on extensive drug perturbation data. We developed a dynamical model representing a cell fate decision network in the AGS gastric cancer cell line, relying on background knowledge extracted from literature and databases. We defined a set of logical equations recapitulating AGS data observed in cells in their baseline proliferative state. Using the modeling software GINsim, model reduction and simulation compression techniques were applied to cope with the vast state space of large logical models and enable simulations of pairwise applications of specific signaling inhibitory chemical substances. Our simulations predicted synergistic growth inhibitory action of five combinations from a total of 21 possible pairs. Four of the predicted synergies were confirmed in AGS cell growth real-time assays, including known effects of combined MEK-AKT or MEK-PI3K inhibitions, along with novel synergistic effects of combined TAK1-AKT or TAK1-PI3K inhibitions. Our strategy reduces the dependence on a priori drug perturbation experimentation for well-characterized signaling networks, by demonstrating that a model predictive of combinatorial drug effects can be inferred from background knowledge on unperturbed and proliferating cancer cells. Our modeling approach can thus contribute to preclinical discovery of efficient anticancer drug combinations, and thereby to development of strategies to tailor treatment to individual cancer patients.

Background: The gastrointestinal peptide hormones cholecystokinin and gastrin exert their biological functions via cholecystokinin receptors CCK1R and CCK2R respectively. Gastrin, a central regulator of gastric acid secretion, is involved in growth and differentiation of gastric and colonic mucosa, and there is evidence that it is pro-carcinogenic. Cholecystokinin is implicated in digestion, appetite control and body weight regulation, and may play a role in several digestive disorders., Results: We performed a detailed analysis of the literature reporting experimental evidence on signaling pathways triggered by CCK1R and CCK2R, in order to create a comprehensive map of gastrin and cholecystokinin-mediated intracellular signaling cascades. The resulting signaling map captures 413 reactions involving 530 molecular species, and incorporates the currently available knowledge into one integrated signaling network. The decomposition of the signaling map into sub-networks revealed 18 modules that represent higher-level structures of the signaling map. These modules allow a more compact mapping of intracellular signaling reactions to known cell behavioral outcomes such as proliferation, migration and apoptosis. The integration of large-scale protein-protein interaction data to this literature-based signaling map in combination with topological analyses allowed us to identify 70 proteins able to increase the compactness of the map. These proteins represent experimentally testable hypotheses for gaining new knowledge on gastrin- and cholecystokinin receptor signaling. The CCKR map is freely available both in a downloadable, machine-readable SBML-compatible format and as a web resource through PAYAO ( http://sblab.celldesigner.org:18080/Payao11/bin/)., Conclusion: We have demonstrated how a literature-based CCKR signaling map together with its protein interaction extensions can be analyzed to generate new hypotheses on molecular mechanisms involved in gastrin- and cholecystokinin-mediated regulation of cellular processes.