IMPRESS-Norway: improving public cancer care by implementing precision medicine in Norway; inclusion rates and preliminary results.

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.

The status of causality in biological databases: data resources and data retrieval possibilities to support logical modeling.

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 Minimum Information about a Molecular Interaction CAusal STatement (MI2CAST).

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.

Improving public cancer care by implementing precision medicine in Norway: IMPRESS-Norway.

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.

Boolean function metrics can assist modelers to check and choose logical rules.

Computational models of biological processes provide one of the most powerful methods for a detailed analysis of the mechanisms that drive the behavior of complex systems. Logic-based modeling has enhanced our understanding and interpretation of those systems. Defining rules that determine how the output activity of biological entities is regulated by their respective inputs has proven to be challenging. Partly this is because of the inherent noise in data that allows multiple model parameterizations to fit the experimental observations, but some of it is also due to the fact that models become increasingly larger, making the use of automated tools to assemble the underlying rules indispensable. We present several Boolean function metrics that provide modelers with the appropriate framework to analyze the impact of a particular model parameterization. We demonstrate the link between a semantic characterization of a Boolean function and its consistency with the model's underlying regulatory structure. We further define the properties that outline such consistency and show that several of the Boolean functions under study violate them, questioning their biological plausibility and subsequent use. We also illustrate that regulatory functions can have major differences with regard to their asymptotic output behavior, with some of them being biased towards specific Boolean outcomes when others are dependent on the ratio between activating and inhibitory regulators. Application results show that in a specific signaling cancer network, the function bias can be used to guide the choice of logical operators for a model that matches data observations. Moreover, graph analysis indicates that commonly used Boolean functions become more biased with increasing numbers of regulators, supporting the idea that rule specification can effectively determine regulatory outcome despite the complex dynamics of biological networks.

CImbinator: a web-based tool for drug synergy analysis in small- and large-scale datasets.

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.

Discovery of Drug Synergies in Gastric Cancer Cells Predicted by Logical Modeling.

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.

Dynamic Boolean modeling of molecular and cellular interactions in psoriasis predicts drug target candidates.

Psoriasis arises from complex interactions between keratinocytes and immune cells, leading to uncontrolled inflammation, immune hyperactivation, and a perturbed keratinocyte life cycle. Despite the availability of drugs for psoriasis management, the disease remains incurable. Treatment response variability calls for new tools and approaches to comprehend the mechanisms underlying disease development. We present a Boolean multiscale population model that captures the dynamics of cell-specific phenotypes in psoriasis, integrating discrete logical formalism and population dynamics simulations. Through simulations and network analysis, the model predictions suggest that targeting neutrophil activation in conjunction with inhibition of either prostaglandin E2 (PGE2) or STAT3 shows promise comparable to interleukin-17 (IL-17) inhibition, one of the most effective treatment options for moderate and severe cases. Our findings underscore the significance of considering complex intercellular interactions and intracellular signaling in psoriasis and highlight the importance of computational approaches in unraveling complex biological systems for drug target identification.

Systematic review: predictive value of organoids in colorectal cancer.

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.

Characterisation of Colorectal Cancer Cell Lines through Proteomic Profiling of Their Extracellular Vesicles.

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.

Functional precision cancer medicine: drug sensitivity screening enabled by cell culture models.

Functional precision medicine is a new, emerging area that can guide cancer treatment by capturing information from direct perturbations of tumor-derived, living cells, such as by drug sensitivity screening. Precision cancer medicine as currently implemented in clinical practice has been driven by genomics, and current molecular tumor boards rely extensively on genomic characterization to advise on therapeutic interventions. However, genomic biomarkers can only guide treatment decisions for a fraction of the patients. In this review we provide an overview of the current state of functional precision medicine, highlight advances for drug-sensitivity screening enabled by cell culture models, and discuss how artificial intelligence (AI) can be coupled to functional precision medicine to guide patient stratification.

Strategies to Enhance Logic Modeling-Based Cell Line-Specific Drug Synergy Prediction.

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.

A Middle-Out Modeling Strategy to Extend a Colon Cancer Logical Model Improves Drug Synergy Predictions in Epithelial-Derived Cancer Cell Lines.

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.

High-throughput screening reveals higher synergistic effect of MEK inhibitor combinations in colon cancer spheroids.

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.

Author Correction: High-throughput screening reveals higher synergistic effect of MEK inhibitor combinations in colon cancer spheroids.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Network and Systems Medicine: Position Paper of the European Collaboration on Science and Technology Action on Open Multiscale Systems Medicine.

Introduction: Network and systems medicine has rapidly evolved over the past decade, thanks to computational and integrative tools, which stem in part from systems biology. However, major challenges and hurdles are still present regarding validation and translation into clinical application and decision making for precision medicine. Methods: In this context, the Collaboration on Science and Technology Action on Open Multiscale Systems Medicine (OpenMultiMed) reviewed the available advanced technologies for multidimensional data generation and integration in an open-science approach as well as key clinical applications of network and systems medicine and the main issues and opportunities for the future. Results: The development of multi-omic approaches as well as new digital tools provides a unique opportunity to explore complex biological systems and networks at different scales. Moreover, the application of findable, applicable, interoperable, and reusable principles and the adoption of standards increases data availability and sharing for multiscale integration and interpretation. These innovations have led to the first clinical applications of network and systems medicine, particularly in the field of personalized therapy and drug dosing. Enlarging network and systems medicine application would now imply to increase patient engagement and health care providers as well as to educate the novel generations of medical doctors and biomedical researchers to shift the current organ- and symptom-based medical concepts toward network- and systems-based ones for more precise diagnoses, interventions, and ideally prevention. Conclusion: In this dynamic setting, the health care system will also have to evolve, if not revolutionize, in terms of organization and management.

Tumor Targeting by αvß3-Integrin-Specific Lipid Nanoparticles Occurs via Phagocyte Hitchhiking.

Although the first nanomedicine was clinically approved more than two decades ago, nanoparticles' (NP) in vivo behavior is complex and the immune system's role in their application remains elusive. At present, only passive-targeting nanoformulations have been clinically approved, while more complicated active-targeting strategies typically fail to advance from the early clinical phase stage. This absence of clinical translation is, among others, due to the very limited understanding for in vivo targeting mechanisms. Dynamic in vivo phenomena such as NPs' real-time targeting kinetics and phagocytes' contribution to active NP targeting remain largely unexplored. To better understand in vivo targeting, monitoring NP accumulation and distribution at complementary levels of spatial and temporal resolution is imperative. Here, we integrate in vivo positron emission tomography/computed tomography imaging with intravital microscopy and flow cytometric analyses to study αvß3-integrin-targeted cyclic arginine-glycine-aspartate decorated liposomes and oil-in-water nanoemulsions in tumor mouse models. We observed that ligand-mediated accumulation in cancerous lesions is multifaceted and identified "NP hitchhiking" with phagocytes to contribute considerably to this intricate process. We anticipate that this understanding can facilitate rational improvement of nanomedicine applications and that immune cell-NP interactions can be harnessed to develop clinically viable nanomedicine-based immunotherapies.