Alvaro Köhn-Luque

• Rockne, Russell C.; Andersen, Morten; Anderson, Alexander R. A.; Basanta, David; Bentivegna, Angela & Benzekry, Sebastien [Vis alle 19 forfattere av denne artikkelen] (2026). The future of mathematical oncology in the age of AI. npj Systems Biology and Applications. 12(1). doi: 10.1038/s41540-026-00656-9. Fulltekst i vitenarkiv Vis sammendrag

  This perspective article discusses emerging advances at the interface of mechanistic modeling and data-driven machine learning, highlighting opportunities for AI to accelerate discovery, improve predictive modeling, and enhance clinical decision-making. We address critical limitations of current AI approaches and propose a perspective on a future where AI augments mechanistic rigor, clinical relevance, and human creativity under the umbrella of a redefined understanding of Mathematical Oncology.

• Azimzade, Youness; Haugen, Mads; Kristensen, Vessela N.; Frigessi, Arnoldo & Luque, Alvaro Kohn (2026). Explainable machine learning-guided integrated multiomics analysis reveals macrophage-driven immune suppression in breast cancer. Nature Communications. 17(1). doi: 10.1038/s41467-026-73617-9. Fulltekst i vitenarkiv Vis sammendrag

  Despite thorough characterizations of cellular compositions within the breast tumor microenvironment (TME), their implications for disease progression and patient prognosis remain poorly understood. Unraveling these effects is vital for identifying potential targets to improve treatment outcomes. In this study, we devise an explainable machine learning (XML) pipeline to scrutinize the associations between TME cellular constituents and relapse-free survival (RFS). By applying this pipeline to estimated cell fractions in the METABRIC and TCGA datasets and comparing these results with associations to pathological complete response (pCR) after neoadjuvant chemotherapy (NAC), we create a comprehensive catalog of the TME’s role based on 5000 patient samples. Our findings reveal an unexpected dichotomy in which macrophages correlate positively with pCR but negatively with RFS, particularly within estrogen receptor-positive (ER+) and Luminal A and B (LumA/B) cancer subtypes. We show that this pattern is driven by heterogeneity in breast tumors characterized by increasing levels of macrophage infiltration. Through imaging mass cytometry (IMC) data analysis, we find that macrophages tend to accumulate in the vicinity of HLA-ABChi epithelial cells as their frequency increases in tumor tissues and that they also express elevated levels of HLA-ABC protein. In both IMC and single-cell RNA sequencing (scRNA-seq) data, we uncover a significant association between these HLA-ABChi macrophages and regulatory and exhausted T cells (TReg and TEx), suggesting their involvement in immune suppression, likely by creating a chronically activated immunosuppressive TME. Subsequent cell-cell communication analysis predicts interactions between HLA-ABChi macrophages and TEx cells via the ligands SIGLEC9, ALCAM, and CSF1, and with TReg cells through APP, ANGPTL4, and SIGLEC9 signaling. Considering the clinical relevance of macrophages in ER+ (LumA/B) subtypes, this work enhances the characterization of macrophage-associated immune suppression in these tumors and identifies potential targets for immunomodulatory strategies.

• Wickstrøm, Kristin; Holten, Aleksander Rygh; Prebensen, Christian Haugland; Luque, Alvaro Kohn; Vitelli, Valeria & Amundsen, Erik Koldberg (2025). Sysmex Cell Population Data for Diagnosing Infection in Patients With Suspected Sepsis in the Emergency Department. International Journal of Laboratory Hematology (IJLH). ISSN 1751-5521. 48(1), s. 71–80. doi: 10.1111/ijlh.70008. Fulltekst i vitenarkiv Vis sammendrag

  Objectives: Early diagnosis of suspected sepsis is crucial to improve patient survival. Cell population (CP) data, a set of leuco-cyte research parameters from hematology instruments, has a potential as markers for infection. The aim of this study was toinvestigate the diagnostic accuracy for infection of CP variables from Sysmex XN instruments in patients with suspected sepsisin the emergency department (ED). Methods: Adult patients with suspected sepsis in the ED were included. CP variables, C-reactive protein (CRP), and post hocassessments of infection were recorded. Logistic regression and machine learning methods were used to develop multivariablemodels, which were evaluated by area under the receiver operating curve (AUC) and calibration plots. Results: The development cohort and the validation cohort consisted of 600 and 656 patients, respectively. Univariate analysesrevealed that complexity in monocytes (MO-X); AUC of 0.78 (0.74, 0.82), reactivity intensity of neutrophils (NEUT-RI); 0.72 (0.67,0.76), and CRP 0.87 (0.84, 0.90) had the highest diagnostic accuracy for infection. A final multivariable model (the optimal model)using Multilayer perceptron (MLP), including MO-X, NEUT-RI, monocyte size (MO-Z), and neutrophil size (NE-FSC), had anAUC of 0.86 (0.85, 0.87) in the development cohort and 0.78 (0.74, 0.82) in the validation cohort with reasonable calibration.Including CRP in this model further improved accuracy and calibration. Conclusions: Sysmex CP variables may help diagnose infections in the ED. However, the lack of well-described calibration pro-cedures and quality assurance for non-IVD approved CP variables is an impediment to clinical implementation

• Schmiester, Leonard; Brasó-Maristany, Fara; González-Farré, Blanca; Pascual, Tomás; Gavilá, Joaquín & Tekpli, Xavier [Vis alle 11 forfattere av denne artikkelen] (2024). Computational Model Predicts Patient Outcomes in Luminal B Breast Cancer Treated with Endocrine Therapy and CDK4/6 Inhibition. Clinical Cancer Research. ISSN 1078-0432. 30(17), s. 3779–3787. doi: 10.1158/1078-0432.CCR-24-0244. Fulltekst i vitenarkiv Vis sammendrag

  Purpose: Development of a computational biomarker to predict, prior to treatment, the response to CDK4/6 inhibition (CDK4/6i) in combination with endocrine therapy in patients with breast cancer. Experimental Design: A mechanistic mathematical model that accounts for protein signaling and drug mechanisms of action was developed and trained on extensive, publicly available data from breast cancer cell lines. The model was built to provide a patient-specific response score based on the expression of six genes (CCND1, CCNE1, ESR1, RB1, MYC, and CDKN1A). The model was validated in five independent cohorts of 148 patients in total with early-stage or advanced breast cancer treated with endocrine therapy and CDK4/6i. Response was measured either by evaluating Ki67 levels and PAM50 risk of relapse (ROR) after neoadjuvant treatment or by evaluating progression-free survival (PFS). Results: The model showed significant association with patient’s outcomes in all five cohorts. The model predicted high Ki67 [area under the curve; AUC (95% confidence interval, CI) of 0.80 (0.64–0.92), 0.81 (0.60–1.00) and 0.80 (0.65–0.93)] and high PAM50 ROR [AUC of 0.78 (0.64–0.89)]. This observation was not obtained in patients treated with chemotherapy. In the other cohorts, patient stratification based on the model prediction was significantly associated with PFS [hazard ratio (HR) = 2.92 (95% CI, 1.08–7.86), P = 0.034 and HR = 2.16 (1.02 4.55), P = 0.043]. Conclusions: A mathematical modeling approach accurately predicts patient outcome following CDK4/6i plus endocrine therapy that marks a step toward more personalized treatments in patients with Luminal B breast cancer.

• Wu, Chenyu; Gunnarsson, Einar Bjarki; Myklebust, Even Moa; Luque, Alvaro Kohn; Tadele, Dagim Shiferaw & Enserink, Jorrit [Vis alle 9 forfattere av denne artikkelen] (2024). Using birth-death processes to infer tumor subpopulation structure from live-cell imaging drug screening data. PLoS Computational Biology. ISSN 1553-734X. 20(3). doi: 10.1371/journal.pcbi.1011888. Fulltekst i vitenarkiv Vis sammendrag

  Tumor heterogeneity is a complex and widely recognized trait that poses significant challenges in developing effective cancer therapies. In particular, many tumors harbor a variety of subpopulations with distinct therapeutic response characteristics. Characterizing this heterogeneity by determining the subpopulation structure within a tumor enables more precise and successful treatment strategies. In our prior work, we developed PhenoPop, a computational framework for unravelling the drug-response subpopulation structure within a tumor from bulk high-throughput drug screening data. However, the deterministic nature of the underlying models driving PhenoPop restricts the model fit and the information it can extract from the data. As an advancement, we propose a stochastic model based on the linear birth-death process to address this limitation. Our model can formulate a dynamic variance along the horizon of the experiment so that the model uses more information from the data to provide a more robust estimation. In addition, the newly proposed model can be readily adapted to situations where the experimental data exhibits a positive time correlation. We test our model on simulated data (in silico) and experimental data (in vitro), which supports our argument about its advantages.

• Metzcar, John; Jutzeler, Catherine R.; Macklin, Paul; Luque, Alvaro Kohn & Brüningk, Sarah C. (2024). A review of mechanistic learning in mathematical oncology. Frontiers in Immunology. 15. doi: 10.3389/fimmu.2024.1363144. Fulltekst i vitenarkiv Vis sammendrag

  Mechanistic learning refers to the synergistic combination of mechanistic mathematical modeling and data-driven machine or deep learning. This emerging field finds increasing applications in (mathematical) oncology. This review aims to capture the current state of the field and provides a perspective on how mechanistic learning may progress in the oncology domain. We highlight the synergistic potential of mechanistic learning and point out similarities and differences between purely data-driven and mechanistic approaches concerning model complexity, data requirements, outputs generated, and interpretability of the algorithms and their results. Four categories of mechanistic learning (sequential, parallel, extrinsic, intrinsic) of mechanistic learning are presented with specific examples. We discuss a range of techniques including physics-informed neural networks, surrogate model learning, and digital twins. Example applications address complex problems predominantly from the domain of oncology research such as longitudinal tumor response predictions or time-to-event modeling. As the field of mechanistic learning advances, we aim for this review and proposed categorization framework to foster additional collaboration between the data- and knowledge-driven modeling fields. Further collaboration will help address difficult issues in oncology such as limited data availability, requirements of model transparency, and complex input data which are embraced in a mechanistic learning framework

• Yildiz, Tugba Akman; Arendt, Lisa M.; Geisler, Jürgen; Kristensen, Vessela N.; Frigessi, Arnoldo & Luque, Alvaro Kohn (2024). Modeling of Mouse Experiments Suggests that Optimal Anti-Hormonal Treatment for Breast Cancer is Diet-Dependent. Bulletin of Mathematical Biology. ISSN 0092-8240. 86:42(4), s. 1–33. doi: 10.1007/s11538-023-01253-1. Fulltekst i vitenarkiv Vis sammendrag

  Estrogen receptor positive breast cancer is frequently treated with anti-hormonal treatment such as aromatase inhibitors (AI). Interestingly, a high body mass index has been shown to have a negative impact on AI efficacy, most likely due to disturbances in steroid metabolism and adipokine production. Here, we propose a mathematical model based on a system of ordinary differential equations to investigate the effect of high-fat diet on tumor growth. We inform the model with data from mouse experiments, where the animals are fed with high-fat or control (normal) diet. By incorporating AI treatment with drug resistance into the model and by solving optimal control problems we found differential responses for control and high-fat diet. To the best of our knowledge, this is the first attempt to model optimal anti-hormonal treatment for breast cancer in the presence of drug resistance. Our results underline the importance of considering high-fat diet and obesity as factors influencing clinical outcomes during anti-hormonal therapies in breast cancer patients.

• Ghannoum, Salim; Fantini, Damiano; Zahoor, Muhammad; Reiterer, Veronika; Phuyal, Santosh & Netto, Waldir Leoncio [Vis alle 13 forfattere av denne artikkelen] (2023). A combined experimental-computational approach uncovers a role for the Golgi matrix protein Giantin in breast cancer progression. PLoS Computational Biology. ISSN 1553-734X. 19(4). doi: 10.1371/journal.pcbi.1010995. Fulltekst i vitenarkiv Vis sammendrag

  Our understanding of how speed and persistence of cell migration affects the growth rate and size of tumors remains incomplete. To address this, we developed a mathematical model wherein cells migrate in two-dimensional space, divide, die or intravasate into the vasculature. Exploring a wide range of speed and persistence combinations, we find that tumor growth positively correlates with increasing speed and higher persistence. As a biologically relevant example, we focused on Golgi fragmentation, a phenomenon often linked to alterations of cell migration. Golgi fragmentation was induced by depletion of Giantin, a Golgi matrix protein, the downregulation of which correlates with poor patient survival. Applying the experimentally obtained migration and invasion traits of Giantin depleted breast cancer cells to our mathematical model, we predict that loss of Giantin increases the number of intravasating cells. This prediction was validated, by showing that circulating tumor cells express significantly less Giantin than primary tumor cells. Altogether, our computational model identifies cell migration traits that regulate tumor progression and uncovers a role of Giantin in breast cancer progression.

• Luque, Alvaro Kohn; Myklebust, Even Moa; Tadele, Dagim Shiferaw; Giliberto, Mariaserena; Schmiester, Leonard & Noory, Jasmine [Vis alle 15 forfattere av denne artikkelen] (2023). Phenotypic deconvolution in heterogeneous cancer cell populations using drug-screening data. Cell Reports Methods. 3(3). doi: 10.1016/j.crmeth.2023.100417. Fulltekst i vitenarkiv Vis sammendrag

  Tumor heterogeneity is an important driver of treatment failure in cancer since therapies often select for drug-tolerant or drug-resistant cellular subpopulations that drive tumor growth and recurrence. Profiling the drug-response heterogeneity of tumor samples using traditional genomic deconvolution methods has yielded limited results, due in part to the imperfect mapping between genomic variation and functional characteristics. Here, we leverage mechanistic population modeling to develop a statistical framework for profiling phenotypic heterogeneity from standard drug-screen data on bulk tumor samples. This method, called PhenoPop, reliably identifies tumor subpopulations exhibiting differential drug responses and estimates their drug sensitivities and frequencies within the bulk population. We apply PhenoPop to synthetically generated cell populations, mixed cell-line experiments, and multiple myeloma patient samples and demonstrate how it can provide individualized predictions of tumor growth under candidate therapies. This methodology can also be applied to deconvolution problems in a variety of biological settings beyond cancer drug response.

• Mo, Torgeir; Brandal, Siri Helene Bertelsen; Luque, Alvaro Kohn; Engebråten, Olav; Kristensen, Vessela N. & Fleischer, Thomas [Vis alle 8 forfattere av denne artikkelen] (2022). Quantification of Tumor Hypoxia through Unsupervised Modelling of Consumption and Supply Hypoxia MR Imaging in Breast Cancer. Cancers. 14(5), s. 1–16. doi: 10.3390/cancers14051326. Fulltekst i vitenarkiv Vis sammendrag

  Simple Summary Hypoxia in solid tumors is common in most solid cancers and is associated with treatment resistance to both chemo- and radiation-therapy. There is also reason to believe that hypoxia is an important determinant of metastic disease. Identifying hypoxia in solid tumors is important in treatment planning and decision making. In 2018 Hompland et al. proposed a method, based on quantifying consumption and supply of oxygen from diffusion weighted magnetic resonance imaging, to estimate the hypoxic fraction of a solid tumor. The method was based on training model parameters on a known hypoxia state in prostate cancer. In the present study we verified the validity of the consumption and supply concept in breast cancer. Furthermore, we developed and validated a new approach to the concept that does not require a ground truth to train the parameters. Abstract The purpose of the present study is to investigate if consumption and supply hypoxia (CSH) MR-imaging can depict breast cancer hypoxia, using the CSH-method initially developed for prostate cancer. Furthermore, to develop a generalized pan-cancer application of the CSH-method that doesn’t require a hypoxia reference standard for training the CSH-parameters. In a cohort of 69 breast cancer patients, we generated, based on the principles of intravoxel incoherent motion modelling, images reflecting cellular density (apparent diffusion coefficient; ADC) and vascular density (perfusion fraction; fp). Combinations of the information in these images were compared to a molecular hypoxia score made from gene expression data, aiming to identify a way to apply the CSH-methodology in breast cancer. Attempts to adapt previously proposed models for prostate cancer included direct transfers and model parameter rescaling. A novel approach, based on rescaling ADC and fp data to give more nuanced response in the relevant physiologic range, was also introduced. The new CSH-method was validated in a prostate cancer cohort with known hypoxia status. The proposed CSH-method gave estimates of hypoxia that was strongly correlated to the molecular hypoxia score in breast cancer, and hypoxia as measured in pathology slices stained with pimonidazole in prostate cancer. The generalized approach to CSH-imaging depicted hypoxia in both breast and prostate cancers and requires no model training. It is easy to implement using readily available technology and encourages further investigation of CSH-imaging in other cancer entities and in other settings, with the goal being to overcome hypoxia-induced resistance to treatment. Keywords: hypoxia; magnetic resonance imaging; diffusion weighted imaging; CSH

• Pesonen, Henri; Simola, Umberto; Luque, Alvaro Kohn; Vuollekoski, Henri; Lai, Xiaoran & Frigessi, Arnoldo [Vis alle 11 forfattere av denne artikkelen] (2022). ABC of the future. International Statistical Review. ISSN 0306-7734. doi: 10.1111/insr.12522. Fulltekst i vitenarkiv Vis sammendrag

  Approximate Bayesian computation (ABC) has advanced in two decades from a seminal idea to a practically applicable inference tool for simulator-based statistical models, which are becoming increasingly popular in many research domains. The computational feasibility of ABC for practical applications has been recently boosted by adopting techniques from machine learning to build surrogate models for the approximate likelihood or posterior and by the introduction of a general-purpose software platform with several advanced features, including automated parallelisation. Here we demonstrate the strengths of the advances in ABC by going beyond the typical benchmark examples and considering real applications in astronomy, infectious disease epidemiology, personalised cancer therapy and financial prediction. We anticipate that the emerging success of ABC in producing actual added value and quantitative insights in the real world will continue to inspire a plethora of further applications across different fields of science, social science and technology.

• Lai, Xiaoran; Taskén, Håkon Austlid; Mo, Torgeir; Funke, Simon Wolfgang; Frigessi, Arnoldo & Rognes, Marie Elisabeth [Vis alle 7 forfattere av denne artikkelen] (2021). A scalable solver for a stochastic, hybrid cellular automaton model of personalized breast cancer therapy. International Journal for Numerical Methods in Biomedical Engineering. ISSN 2040-7939. 38(1). doi: 10.1002/cnm.3542. Fulltekst i vitenarkiv Vis sammendrag

  Mathematical modeling and simulation is a promising approach to personalizedcancer medicine. Yet, the complexity, heterogeneity and multi-scale nature ofcancer pose significant computational challenges. Coupling discrete cell-basedmodels with continuous models using hybrid cellular automata (CA) is a power-ful approach for mimicking biological complexity and describing the dynamicalexchange of information across different scales. However, when clinically rele-vant cancer portions are taken into account, such models become computation-ally very expensive. While efficient parallelization techniques for continuousmodels exist, their coupling with discrete models, particularly CA, necessitatesmore elaborate solutions. Building upon FEniCS, a popular and powerful scien-tific computing platform for solving partial differential equations, we developedparallel algorithms to link stochastic CA with differential equations (https://bitbucket.org/HTasken/cansim). The algorithms minimize the communicationbetween processes that share CA neighborhood values while also allowing forreproducibility during stochastic updates. We demonstrated the potential of oursolution on a complex hybrid cellular automaton model of breast cancer treatedwith combination chemotherapy. On a single-core processor, we obtained nearlylinear scaling with an increasing problem size, whereas weak parallel scalingshowed moderate growth in solving time relative to increase in problem size.Finally, we applied the algorithm to a problem that is 500 times larger than previ-ous work, allowing us to run personalized therapy simulations based on hetero-geneous cell density and tumor perfusion conditions estimated from magneticresonance imaging data on an unprecedented scale

• Möller, Thomas; Klungerbo, Vibeke; Diab, Simone; Holmstrøm, Henrik; Edvardsen, Elisabeth & Grindheim, Guro [Vis alle 11 forfattere av denne artikkelen] (2021). Circulatory Response to Rapid Volume Expansion and Cardiorespiratory Fitness in Fontan Circulation. Pediatric Cardiology. ISSN 0172-0643. doi: 10.1007/s00246-021-02802-y. Fulltekst i vitenarkiv Vis sammendrag

  The role of dysfunction of the single ventricle in Fontan failure is incompletely understood. We aimed to evaluate hemodynamic responses to preload increase in Fontan circulation, to determine whether circulatory limitations in different locations identified by experimental preload increase are associated with cardiorespiratory fitness (CRF), and to assess the impact of left versus right ventricular morphology. In 38 consecutive patients (median age = 16.6 years, 16 females), heart catheterization was supplemented with a rapid 5-mL/kg body weight volume expansion. Central venous pressure (CVP), ventricular end-diastolic pressure (VEDP), and peak systolic pressure were averaged for 15‒30 s, 45‒120 s, and 4‒6 min (steady state), respectively. CRF was assessed by peak oxygen consumption (VO2peak) and ventilatory threshold (VT). Median CVP increased from 13 mmHg at baseline to 14.5 mmHg (p < 0.001) at steady state. CVP increased by more than 20% in eight patients. Median VEDP increased from 10 mmHg at baseline to 11.5 mmHg (p < 0.001). Ten patients had elevated VEDP at steady state, and in 21, VEDP increased more than 20%. The transpulmonary pressure difference (CVP‒VEDP) and CVP were consistently higher in patients with right ventricular morphology across repeated measurements. CVP at any stage was associated with VO2peak and VT. VEDP after volume expansion was associated with VT. Preload challenge demonstrates the limitations beyond baseline measurements. Elevation of both CVP and VEDP are associated with impaired CRF. Transpulmonary flow limitation was more pronounced in right ventricular morphology. Ventricular dysfunction may contribute to functional impairment after Fontan operation in young adulthood.

• Wickstrøm, Kristin Elisabeth; Vitelli, Valeria; Carr, Ewan; Holten, Aleksander R.; Bendayan, Rebecca & Reiner, Andrew Henry [Vis alle 15 forfattere av denne artikkelen] (2021). Regional performance variation in external validation of four prediction models for severity of COVID-19 at hospital admission: An observational multi-centre cohort study. PLOS ONE. 16(8), s. 1–13. doi: 10.1371/journal.pone.0255748. Fulltekst i vitenarkiv Vis sammendrag

  Background: Prediction models should be externally validated to assess their performance before implementation. Several prediction models for coronavirus disease-19 (COVID-19) have been published. This observational cohort study aimed to validate published models of severity for hospitalized patients with COVID-19 using clinical and laboratory predictors. Methods: Prediction models fitting relevant inclusion criteria were chosen for validation. The outcome was either mortality or a composite outcome of mortality and ICU admission (severe disease). 1295 patients admitted with symptoms of COVID-19 at Kings Cross Hospital (KCH) in London, United Kingdom, and 307 patients at Oslo University Hospital (OUH) in Oslo, Norway were included. The performance of the models was assessed in terms of discrimination and calibration. Results: We identified two models for prediction of mortality (referred to as Xie and Zhang1) and two models for prediction of severe disease (Allenbach and Zhang2). The performance of the models was variable. For prediction of mortality Xie had good discrimination at OUH with an area under the receiver-operating characteristic (AUROC) 0.87 [95% confidence interval (CI) 0.79–0.95] and acceptable discrimination at KCH, AUROC 0.79 [0.76–0.82]. In prediction of severe disease, Allenbach had acceptable discrimination (OUH AUROC 0.81 [0.74–0.88] and KCH AUROC 0.72 [0.68–0.75]). The Zhang models had moderate to poor discrimination. Initial calibration was poor for all models but improved with recalibration. Conclusions: The performance of the four prediction models was variable. The Xie model had the best discrimination for mortality, while the Allenbach model had acceptable results for prediction of severe disease.

• Ghannoum, Salim; Netto, Waldir Leoncio; Fantini, Damiano; Ragan-Kelley, Benjamin; Parizadeh, Seyedamirabbas & Jonasson, Emma [Vis alle 9 forfattere av denne artikkelen] (2021). DIscBIO: A User-Friendly Pipeline for Biomarker Discovery in Single-Cell Transcriptomics. International Journal of Molecular Sciences. ISSN 1661-6596. 22(3). doi: 10.3390/ijms22031399. Fulltekst i vitenarkiv Vis sammendrag

  The growing attention toward the benefits of single-cell RNA sequencing (scRNA-seq) is leading to a myriad of computational packages for the analysis of different aspects of scRNA-seq data. For researchers without advanced programing skills, it is very challenging to combine several packages in order to perform the desired analysis in a simple and reproducible way. Here we present DIscBIO, an open-source, multi-algorithmic pipeline for easy, efficient and reproducible analysis of cellular sub-populations at the transcriptomic level. The pipeline integrates multiple scRNA-seq packages and allows biomarker discovery with decision trees and gene enrichment analysis in a network context using single-cell sequencing read counts through clustering and differential analysis. DIscBIO is freely available as an R package. It can be run either in command-line mode or through a user-friendly computational pipeline using Jupyter notebooks. We showcase all pipeline features using two scRNA-seq datasets. The first dataset consists of circulating tumor cells from patients with breast cancer. The second one is a cell cycle regulation dataset in myxoid liposarcoma. All analyses are available as notebooks that integrate in a sequential narrative R code with explanatory text and output data and images. R users can use the notebooks to understand the different steps of the pipeline and will guide them to explore their scRNA-seq data. We also provide a cloud version using Binder that allows the execution of the pipeline without the need of downloading R, Jupyter or any of the packages used by the pipeline. The cloud version can serve as a tutorial for training purposes, especially for those that are not R users or have limited programing skills. However, in order to do meaningful scRNA-seq analyses, all users will need to understand the implemented methods and their possible options and limitations.

• Ghannoum, Salim; Antos, Kamil; Netto, Waldir Leoncio; Gomes, Cecil; Köhn-Luque, Alvaro & Farhan, Hesso (2021). CellMAPtracer: A User-Friendly Tracking Tool for Long-Term Migratory and Proliferating Cells Associated with FUCCI Systems. Cells. 10(2), s. 1–18. doi: 10.3390/cells10020469. Fulltekst i vitenarkiv Vis sammendrag

  Cell migration is a fundamental biological process of key importance in health and disease. Advances in imaging techniques have paved the way to monitor cell motility. An ever-growing collection of computational tools to track cells has improved our ability to analyze moving cells. One renowned goal in the field is to provide tools that track cell movement as comprehensively and automatically as possible. However, fully automated tracking over long intervals of time is challenged by dividing cells, thus calling for a combination of automated and supervised tracking. Furthermore, after the emergence of various experimental tools to monitor cell-cycle phases, it is of relevance to integrate the monitoring of cell-cycle phases and motility. We developed CellMAPtracer, a multiplatform tracking system that achieves that goal. It can be operated as a conventional, automated tracking tool of single cells in numerous imaging applications. However, CellMAPtracer also allows adjusting tracked cells in a semiautomated supervised fashion, thereby improving the accuracy and facilitating the long-term tracking of migratory and dividing cells. CellMAPtracer is available with a user-friendly graphical interface and does not require any coding or programming skills. CellMAPtracer is compatible with two- and three-color fluorescent ubiquitination-based cell-cycle indicator (FUCCI) systems and allows the user to accurately monitor various migration parameters throughout the cell cycle, thus having great potential to facilitate new discoveries in cell biology.

• Carr, Ewan; Bendayan, Rebecca; Bean, Daniel; Stammers, Matt; Wang, Wenjuan & Zhang, Huayu [Vis alle 48 forfattere av denne artikkelen] (2021). Evaluation and improvement of the National Early Warning Score (NEWS2) for COVID-19: a multi-hospital study. BMC Medicine. 19(23), s. 1–16. doi: 10.1186/s12916-020-01893-3. Fulltekst i vitenarkiv Vis sammendrag

  Background The National Early Warning Score (NEWS2) is currently recommended in the UK for the risk stratification of COVID-19 patients, but little is known about its ability to detect severe cases. We aimed to evaluate NEWS2 for the prediction of severe COVID-19 outcome and identify and validate a set of blood and physiological parameters routinely collected at hospital admission to improve upon the use of NEWS2 alone for medium-term risk stratification. Methods Training cohorts comprised 1276 patients admitted to King’s College Hospital National Health Service (NHS) Foundation Trust with COVID-19 disease from 1 March to 30 April 2020. External validation cohorts included 6237 patients from five UK NHS Trusts (Guy’s and St Thomas’ Hospitals, University Hospitals Southampton, University Hospitals Bristol and Weston NHS Foundation Trust, University College London Hospitals, University Hospitals Birmingham), one hospital in Norway (Oslo University Hospital), and two hospitals in Wuhan, China (Wuhan Sixth Hospital and Taikang Tongji Hospital). The outcome was severe COVID-19 disease (transfer to intensive care unit (ICU) or death) at 14 days after hospital admission. Age, physiological measures, blood biomarkers, sex, ethnicity, and comorbidities (hypertension, diabetes, cardiovascular, respiratory and kidney diseases) measured at hospital admission were considered in the models. Results A baseline model of ‘NEWS2 + age’ had poor-to-moderate discrimination for severe COVID-19 infection at 14 days (area under receiver operating characteristic curve (AUC) in training cohort = 0.700, 95% confidence interval (CI) 0.680, 0.722; Brier score = 0.192, 95% CI 0.186, 0.197). A supplemented model adding eight routinely collected blood and physiological parameters (supplemental oxygen flow rate, urea, age, oxygen saturation, C-reactive protein, estimated glomerular filtration rate, neutrophil count, neutrophil/lymphocyte ratio) improved discrimination (AUC = 0.735; 95% CI 0.715, 0.757), and these improvements were replicated across seven UK and non-UK sites. However, there was evidence of miscalibration with the model tending to underestimate risks in most sites. Conclusions NEWS2 score had poor-to-moderate discrimination for medium-term COVID-19 outcome which raises questions about its use as a screening tool at hospital admission. Risk stratification was improved by including readily available blood and physiological parameters measured at hospital admission, but there was evidence of miscalibration in external sites. This highlights the need for a better understanding of the use of early warning scores for COVID.

• Lai, Xiaoran; Geier, Oliver; Fleischer, Thomas; Garred, Øystein; Borgen, Elin & Funke, Simon Wolfgang [Vis alle 14 forfattere av denne artikkelen] (2019). Towards personalized computer simulation of breast cancer treatment: a multi-scale pharmacokinetic and pharmacodynamic model informed by multi-type patient data. Cancer Research. ISSN 0008-5472. 79(16), s. 4293–4304. doi: 10.1158/0008-5472.CAN-18-1804. Fulltekst i vitenarkiv Vis sammendrag

  The usefulness of mechanistic models to disentangle complex multiscale cancer processes, such as treatment response, has been widely acknowledged. However, a major barrier for multiscale models to predict treatment outcomes in individual patients lies in their initialization and parametrization, which needs to reflect individual cancer characteristics accurately. In this study, we use multitype measurements acquired routinely on a single breast tumor, including histopathology, MRI, and molecular profiling, to personalize parts of a complex multiscale model of breast cancer treated with chemotherapeutic and antiangiogenic agents. The model accounts for drug pharmacokinetics and pharmacodynamics. We developed an open-source computer program that simulates cross-sections of tumors under 12-week therapy regimens and used it to individually reproduce and elucidate treatment outcomes of 4 patients. Two of the tumors did not respond to therapy, and model simulations were used to suggest alternative regimens with improved outcomes dependent on the tumor's individual characteristics. It was determined that more frequent and lower doses of chemotherapy reduce tumor burden in a low proliferative tumor while lower doses of antiangiogenic agents improve drug penetration in a poorly perfused tumor. Furthermore, using this model, we were able to correctly predict the outcome in another patient after 12 weeks of treatment. In summary, our model bridges multitype clinical data to shed light on individual treatment outcomes.

• Hinrichs, C. Noreen; Ingargiola, Mirjam; Käubler, Theresa; Löck, Steffen; Temme, Achim & Luque, Alvaro Kohn [Vis alle 10 forfattere av denne artikkelen] (2017). Arginine Deprivation Therapy: Putative Strategy to Eradicate Glioblastoma Cells by Radiosensitization. Molecular Cancer Therapeutics. ISSN 1535-7163. doi: 10.1158/1535-7163.mct-16-0807. Fulltekst i vitenarkiv
• Alfonso, JCL; Luque, Alvaro Kohn; Stylianopoulos, T; Feuerhake, F; Deutsch, A & Hatzikirou, H (2016). Why one-size-fits-all vaso-modulatory interventions fail to control glioma invasion: In silico insights. Scientific Reports. 6. doi: 10.1038/srep37283. Fulltekst i vitenarkiv

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• Lee, Stephanie; Tahiri, Andliena; Gjesdal, Kjell Inge; Lüders, Torben; Reitsma, Laurens Cornelus & Lyngra, Marianne [Vis alle 10 forfattere av denne artikkelen] (2025). Radiogenomics during neoadjuvant therapy with aromatase inhibitors: integrating dynamic magnetic resonance data and molecular biology. Clinical Cancer Research. ISSN 1078-0432. 31(12_Supplement), s. 01–01. doi: 10.1158/1557-3265.sabcs24-p1-01-07. Fulltekst i vitenarkiv Vis sammendrag

  Abstract Background: Neoadjuvant treatment of locally advanced breast cancer (LABC) is now well-established and considerable progress has been made concerning drug development for subgroups of patients. However, reliable predictive markers are still lacking for many patients. Thus, radiological measures like repeated MRI of the breast and axilla, after 3 and 6 months of therapy, are used to follow the patients during neoadjuvant therapy to confirm successful downstaging. Although not established in clinical routine, dynamic MRI contains quantitative information that can provide valuable insight into the tumour microenvironment and, when combined with clinical and molecular data, has the potential to enhance the prediction of tumor responses. Here, we analyze split-dynamic MRI data in relation to clinicopathological and molecular data to further identify disease heterogeneity that can be used to improve prognostic accuracy and inform treatment decisions in breast cancer patients. Methods: This study reports results from eligible patients with ER pos., HER2 neg. LABC that were enrolled in the NEOLETEXE-trial (1). Clinicopathological, molecular, and advanced MRI data were acquired from this trial, collected before, during, and after neoadjuvant endocrine therapy (NET) with aromatase inhibitors given in sequence. MR images were generated using a split-dynamic technique that simultaneously acquires high spatial resolution and high temporal resolution images, providing information on tissue permeability and vascularity. The images underwent post-processing on the nordicICE software where parametric values were extracted based on whole-tumour volumes of interest. These parametric values were analysed in correlation with clinico-pathological data and with molecular findings from bulk RNA-seq and single cell data from a subset of the same patients. Results: From the kinetic MRI data, the volume transfer constant (Ktrans), a parameter reflecting capillary permeability, was significantly reduced after NET (p = 0.005). Bulk RNA-seq data was analysed for gene expression, pathway involvement, and cell type abundance before, during, and after treatment. Gene expression analysis revealed significant dysregulation of genes related to cell proliferation and cell cycle regulation following treatment. Biological pathway analysis revealed significant down-regulation of hemostatic activity and upregulation of cell cycle and interleukin signaling following treatment. Cell type analysis revealed a higher abundance of CD4+ T-cells and T-helper cells following treatment. These results will be further correlated with single cell data as well as measures of treatment response and clinical outcome. Conclusions: Our findings suggest that the addition of MRI data to clinicopathological and molecular data enhances the characterization of tumor responses during NET for locally advanced breast cancer. 1. The NEOLETEXE trial: a neoadjuvant cross-over study exploring the lack of cross resistance between aromatase inhibitors. Bahrami N. et al., Future Oncoloy 15 (32), 3675-3682, 2019. Citation Format: Stephanie Lee, Andliena Tahiri, Kjell-Inge Gjesdal, Torben Lüders, Laurens Reitsma, Marianne Lyngra, Alvaro Köhn-Luque, Vessela Kristensen, Jürgen Geisler, Jonn Terje Geitung. Radiogenomics during neoadjuvant therapy with aromatase inhibitors: integrating dynamic magnetic resonance data and molecular biology [abstract]. In: Proceedings of the San Antonio Breast Cancer Symposium 2024; 2024 Dec 10-13; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(12 Suppl):Abstract nr P1-01-07.

• Gilmour, Brian Christopher; Visser, Johan Georg; Köhn-Luque, Alvaro; Lossius, Andreas & Spurkland, Anne (2024). SH2D2A Is an Indicator of Favourable Prognosis in Bladder Cancer and Is Enriched in Activated Treg Cells. Fulltekst i vitenarkiv
• Rattalma, Arnoldo Frigessi Di; Köhn-Luque, Alvaro; Kristensen, Vessela N.; Gørbitz, Carl Henrik & Engebråten, Olav (2020). Kan statistikk og matematikk redde like mange liv som nye medisiner? Aftenposten (morgenutg. : trykt utg.). ISSN 0804-3116. Fulltekst i vitenarkiv
• Lai, Xiaoran; Luque, Alvaro Kohn; Frigessi, Arnoldo & Geier, Oliver (2017). Breast MRI as a building block for patient-specific simulations of drug effect. Fulltekst i vitenarkiv

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