evenmm

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Currently working on Bayesian nonlinear mixed effect models for relapse prediction in Multiple Myeloma.

In 2023, we developed a new method called PhenoPop that reliably identifies tumor subpopulations exhibiting differential drug responses, and estimates their drug-sensitivities and frequencies within the bulk.

Academic interests

Hierarchical Bayesian modeling
Knowledge-driven statistical learning
Personalized medicine
Multiple Myeloma

Background

MSc (2020) in Industrial Mathematics at the Norwegian University of Science and Technology (NTNU). Master thesis on a model for latent variable inference in neuroscience supervised by Benjamin Dunn.

Collaborators

Alvaro Köhn-Luque and Arnoldo Frigessi at Oslo Centre of Biostatistics and Epidemiology
Jasmine Foo and Kevin Leder at the University of Minnesota, USA
Fredrik Schjesvold at the KG Jebsen Center for B-Cell Malignancies and Oslo Myeloma Center, University of Oslo
Kjetil Taskén and Jorrit Enserink, Institute for Cancer Research at Oslo University Hospital
Helle Skjerven, Drammen sykehus

Pages

Tags: Biostatistics, Machine Learning, Computational Biology, Cancer, Stochastic Modeling, Statistical inference, Personalised Cancer Therapies, Latent variable models, Computational intensive statistics

Wu, Chenyu; Gunnarsson, Einar Bjarki; Myklebust, Even Moa; Luque, Alvaro Kohn; Tadele, Dagim Shiferaw & Enserink, Jorrit [Show all 9 contributors for this article] (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. Full text in Research Archive Show summary
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.
Skjerven, Helle; Myklebust, Even Moa; Korvald, Christian; Stubberud, Kjetil; Hovda, Tone & Porojnicu, Alina Carmen [Show all 10 contributors for this article] (2023). Long-term follow-up of complex oncoplastic breast-conserving surgery, standard breast conservation and skin-sparing mastectomy in DCIS - a register-based study. European Journal of Surgical Oncology. ISSN 0748-7983. 50(2). doi: 10.1016/j.ejso.2023.107938. Full text in Research Archive
Luque, Alvaro Kohn; Myklebust, Even Moa; Tadele, Dagim Shiferaw; Giliberto, Mariaserena; Schmiester, Leonard & Noory, Jasmine [Show all 15 contributors for this article] (2023). Phenotypic deconvolution in heterogeneous cancer cell populations using drug-screening data. Cell Reports Methods. 3(3). doi: 10.1016/j.crmeth.2023.100417. Full text in Research Archive Show summary
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.
Skjerven, Helle; Myklebust, Even Moa; Korvald, Christian; Porojnicu, Alina Carmen; Kåresen, Rolf & Hofvind, Solveig Sand-Hanssen [Show all 8 contributors for this article] (2022). Oncological outcomes after simple and skin-sparing mastectomy of ductal carcinoma in situ: A register-based cohort study of 576 Norwegian women. European Journal of Surgical Oncology. ISSN 0748-7983. doi: 10.1016/j.ejso.2022.11.594. Full text in Research Archive

View all works in NVA

Myklebust, Even Moa (2023). Relapse prediction in Multiple Myeloma patients through Mathematical modeling. Full text in Research Archive
Myklebust, Even Moa (2023). Personalized treatment recommendations for Multiple Myeloma with a Hierarchical Bayesian model. Full text in Research Archive
Myklebust, Even Moa (2023). Predicting treatment response in Multiple Myeloma by combining mechanistic modeling with statistical learning in a Hierarchical Bayesian framework. Full text in Research Archive
Myklebust, Even Moa (2023). A framework for personalized prognosis of tumor evolution in Multiple Myeloma. Full text in Research Archive
Myklebust, Even Moa (2023). Predicting treatment response in Multiple Myeloma by combining mechanistic modeling with statistical learning in a Hierarchical Bayesian framework. Full text in Research Archive
Myklebust, Even Moa (2023). Predicting Progression Free Survival in Multiple Myeloma with a Hierarchical Bayesian model. Full text in Research Archive
Myklebust, Even Moa (2023). Skin-sparing vs simple mastectomy for DCIS. Full text in Research Archive
Myklebust, Even Moa (2023). Relapse prediction in Multiple Myeloma patients through Mathematical modeling. Full text in Research Archive
Myklebust, Even Moa (2022). A framework for personalized prognosis of tumor evolution in Multiple Myeloma by multi-output statistical learning. Full text in Research Archive
Myklebust, Even Moa (2022). Phenotypic deconvolution in heterogeneous cancer cell populations using drug screening data. Full text in Research Archive
Myklebust, Even Moa (2022). Phenotypic deconvolution in heterogeneous cancer cell populations using drug screening data. Full text in Research Archive
Myklebust, Even Moa (2022). Phenotypic deconvolution in heterogeneous cancer cell populations using drug screening data. Full text in Research Archive
Myklebust, Even Moa (2022). Phenotypic deconvolution in heterogeneous cancer cell populations using drug screening data. Full text in Research Archive
Myklebust, Even Moa (2021). Phenotypic deconvolution of cancer drug screens. Full text in Research Archive

View all works in NVA

Published Mar. 15, 2023 3:53 PM - Last modified Jan. 10, 2025 10:34 AM