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Article ; Online: Modeling of Mouse Experiments Suggests that Optimal Anti-Hormonal Treatment for Breast Cancer is Diet-Dependent.

Akman, Tuğba / Arendt, Lisa M / Geisler, Jürgen / Kristensen, Vessela N / Frigessi, Arnoldo / Köhn-Luque, Alvaro

2024 Volume 86, Issue 4, Page(s) 42

Abstract: 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 ... ...

Abstract	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.
MeSH term(s)	Humans ; Animals ; Mice ; Female ; Breast Neoplasms/pathology ; Drug Resistance, Neoplasm ; Models, Biological ; Mathematical Concepts ; Aromatase Inhibitors/therapeutic use ; Aromatase Inhibitors/pharmacology ; Diet
Chemical Substances	Aromatase Inhibitors
Language	English
Publishing date	2024-03-18
Publishing country	United States
Document type	Journal Article
ZDB-ID	184905-0
ISSN	1522-9602 ; 0007-4985 ; 0092-8240
ISSN (online)	1522-9602
ISSN	0007-4985 ; 0092-8240
DOI	10.1007/s11538-023-01253-1
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Article ; Online: The Antigenicity of the Tumor Cell - Context Matters.

Kristensen, Vessela N

The New England journal of medicine

2017 Volume 376, Issue 5, Page(s) 491–493

MeSH term(s)	Antigens, Neoplasm/immunology ; Blood Donors ; Epitopes, T-Lymphocyte/genetics ; Epitopes, T-Lymphocyte/immunology ; Humans ; Immunotherapy ; Lymphocytes, Tumor-Infiltrating/immunology ; Melanoma/genetics ; Melanoma/immunology ; Melanoma/therapy ; Mutation ; Neoplasms/immunology ; RNA, Messenger ; Receptors, Antigen, T-Cell/immunology ; T-Lymphocytes/immunology
Chemical Substances	Antigens, Neoplasm ; Epitopes, T-Lymphocyte ; RNA, Messenger ; Receptors, Antigen, T-Cell
Language	English
Publishing date	2017--02
Publishing country	United States
Document type	Journal Article
ZDB-ID	207154-x
ISSN	1533-4406 ; 0028-4793
ISSN (online)	1533-4406
ISSN	0028-4793
DOI	10.1056/NEJMcibr1613793
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Article ; Online: The breast cancer coagulome in the tumor microenvironment and its role in prognosis and treatment response to chemotherapy.

Tinholt, Mari / Tekpli, Xavier / Torland, Lilly Anne / Tahiri, Andliena / Geisler, Jürgen / Kristensen, Vessela / Sandset, Per Morten / Iversen, Nina

Journal of thrombosis and haemostasis : JTH

2024 Volume 22, Issue 5, Page(s) 1319–1335

Abstract: Background: The procoagulant phenotype in cancer is linked to thrombosis, cancer progression, and immune response. A novel treatment that reduces the risk of both thrombosis and cancer progression without excess bleeding risk remains to be identified.!## ...

Abstract	Background: The procoagulant phenotype in cancer is linked to thrombosis, cancer progression, and immune response. A novel treatment that reduces the risk of both thrombosis and cancer progression without excess bleeding risk remains to be identified. Objectives: Here, we aimed to broadly investigate the breast tumor coagulome and its relation to prognosis, treatment response to chemotherapy, and the tumor microenvironment. Methods: Key coagulation-related genes (n = 35) were studied in a Norwegian cohort with tumor (n = 134) and normal (n = 189) tissue and in the Cancer Genome Atlas (n = 1052) data set. We performed gene set variation analysis in the Norwegian cohort, and in the Cancer Genome Atlas cohort, associations with the tumor microenvironment and prognosis were evaluated. Analyses were performed with cBioPortal, Estimation of Stromal and Immune cells in Malignant Tumors Using Expression Data, Tumor Immune Estimation Resource, the integrated repository portal for tumor-immune system interactions, Tumor Immune Single-cell Hub 2, and the receiver operating characteristic plotter. Six independent breast cancer cohorts were used to study the tumor coagulome and treatment response to chemotherapy. Results: Twenty-two differentially expressed coagulation-related genes were identified in breast tumors. Several coagulome factors were correlated with tumor microenvironment characteristics and were expressed by nonmalignant cells in the tumor microenvironment. PLAT and F8 were independent predictors of better overall survival and progression-free survival, respectively. F12 and PLAU were predictors of worse progression-free survival. The PROCR-THBD-PLAT signature showed a promising predictive value (area under the curve, 0.75; 95% CI, 0.69-0.81; P = 3.6 × 10 Conclusion: The breast tumor coagulome showed potential in prediction of prognosis and chemotherapy response. Cells within the tumor microenvironment are sources of coagulome factors and may serve as therapeutic targets of coagulation factors.
MeSH term(s)	Humans ; Tumor Microenvironment ; Breast Neoplasms/drug therapy ; Breast Neoplasms/genetics ; Female ; Blood Coagulation/drug effects ; Middle Aged ; Biomarkers, Tumor/genetics ; Biomarkers, Tumor/blood ; Treatment Outcome ; Norway ; Prognosis ; Gene Expression Regulation, Neoplastic ; Antineoplastic Combined Chemotherapy Protocols/therapeutic use ; Aged ; Blood Coagulation Factors/genetics ; Adult
Chemical Substances	Biomarkers, Tumor ; Blood Coagulation Factors
Language	English
Publishing date	2024-01-17
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2112661-6
ISSN	1538-7836 ; 1538-7933
ISSN (online)	1538-7836
ISSN	1538-7933
DOI	10.1016/j.jtha.2024.01.003
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Article ; Online: Analysis of Spatial Molecular Data.

Levy-Jurgenson, Alona / Tekpli, Xavier / Kristensen, Vessela N / Yakhini, Zohar

Methods in molecular biology (Clifton, N.J.)

2022 Volume 2614, Page(s) 349–356

Abstract: Digital analysis of pathology whole-slide images has been recently gaining interest in the context of cancer diagnosis and treatment. In particular, deep learning methods have demonstrated significant potential in supporting pathology analysis, recently ... ...

Abstract	Digital analysis of pathology whole-slide images has been recently gaining interest in the context of cancer diagnosis and treatment. In particular, deep learning methods have demonstrated significant potential in supporting pathology analysis, recently detecting molecular traits never before recognized in pathology H&E whole-slide images (WSIs). Alongside these advancements in the digital analysis of WSIs, it is becoming increasingly evident that both spatial and overall tumor heterogeneity may be significant determinants of cancer prognosis and treatment outcome. In this chapter, we describe methods that aim to support these two elements. We describe both an end-to-end deep learning pipeline for producing limited spatial transcriptomics from WSIs with associated bulk gene expression data, as well as an algorithm for quantifying spatial tumor heterogeneity based on the results of this pipeline.
MeSH term(s)	Humans ; Neoplasms/diagnosis ; Neoplasms/genetics ; Phenotype ; Algorithms ; Microscopy/methods
Language	English
Publishing date	2022-12-29
Publishing country	United States
Document type	Journal Article
ISSN	1940-6029
ISSN (online)	1940-6029
DOI	10.1007/978-1-0716-2914-7_20
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Article ; Online: Efficient gene expression signature for a breast cancer immuno-subtype.

Galili, Ben / Tekpli, Xavier / Kristensen, Vessela N / Yakhini, Zohar

PloS one

2021 Volume 16, Issue 1, Page(s) e0245215

Abstract: Motivation and background: The patient's immune system plays an important role in cancer pathogenesis, prognosis and susceptibility to treatment. Recent work introduced an immune related breast cancer. This subtyping is based on the expression profiles ... ...

Abstract	Motivation and background: The patient's immune system plays an important role in cancer pathogenesis, prognosis and susceptibility to treatment. Recent work introduced an immune related breast cancer. This subtyping is based on the expression profiles of the tumor samples. Specifically, one study showed that analyzing 658 genes can lead to a signature for subtyping tumors. Furthermore, this classification is independent of other known molecular and clinical breast cancer subtyping. Finally, that study shows that the suggested subtyping has significant prognostic implications. Results: In this work we develop an efficient signature associated with survival in breast cancer. We begin by developing a more efficient signature for the above-mentioned breast cancer immune-based subtyping. This signature represents better performance with a set of 579 genes that obtains an improved Area Under Curve (AUC). We then determine a set of 193 genes and an associated classification rule that yield subtypes with a much stronger statistically significant (log rank p-value < 2 × 10-4 in a test cohort) difference in survival. To obtain these improved results we develop a feature selection process that matches the high-dimensionality character of the data and the dual performance objectives, driven by survival and anchored by the literature subtyping.
MeSH term(s)	Biomarkers, Tumor/immunology ; Breast Neoplasms/classification ; Breast Neoplasms/immunology ; Breast Neoplasms/mortality ; Disease-Free Survival ; Female ; Gene Expression Regulation, Neoplastic/immunology ; Humans ; Survival Rate ; Transcriptome/immunology
Chemical Substances	Biomarkers, Tumor
Language	English
Publishing date	2021-01-12
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ISSN	1932-6203
ISSN (online)	1932-6203
DOI	10.1371/journal.pone.0245215
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Article ; Online: Transcriptomic pan-cancer analysis using rank-based Bayesian inference.

Vitelli, Valeria / Fleischer, Thomas / Ankill, Jørgen / Arjas, Elja / Frigessi, Arnoldo / Kristensen, Vessela N / Zucknick, Manuela

Molecular oncology

2023 Volume 17, Issue 4, Page(s) 548–563

Abstract: The analysis of whole genomes of pan-cancer data sets provides a challenge for researchers, and we contribute to the literature concerning the identification of robust subgroups with clear biological interpretation. Specifically, we tackle this ... ...

Abstract	The analysis of whole genomes of pan-cancer data sets provides a challenge for researchers, and we contribute to the literature concerning the identification of robust subgroups with clear biological interpretation. Specifically, we tackle this unsupervised problem via a novel rank-based Bayesian clustering method. The advantages of our method are the integration and quantification of all uncertainties related to both the input data and the model, the probabilistic interpretation of final results to allow straightforward assessment of the stability of clusters leading to reliable conclusions, and the transparent biological interpretation of the identified clusters since each cluster is characterized by its top-ranked genomic features. We applied our method to RNA-seq data from cancer samples from 12 tumor types from the Cancer Genome Atlas. We identified a robust clustering that mostly reflects tissue of origin but also includes pan-cancer clusters. Importantly, we identified three pan-squamous clusters composed of a mix of lung squamous cell carcinoma, head and neck squamous carcinoma, and bladder cancer, with different biological functions over-represented in the top genes that characterize the three clusters. We also found two novel subtypes of kidney cancer that show different prognosis, and we reproduced known subtypes of breast cancer. Taken together, our method allows the identification of robust and biologically meaningful clusters of pan-cancer samples.
MeSH term(s)	Humans ; Female ; Transcriptome ; Bayes Theorem ; Carcinoma, Squamous Cell/genetics ; Breast Neoplasms/genetics ; Cluster Analysis ; Head and Neck Neoplasms
Language	English
Publishing date	2023-01-23
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2415106-3
ISSN	1878-0261 ; 1574-7891
ISSN (online)	1878-0261
ISSN	1574-7891
DOI	10.1002/1878-0261.13354
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Book ; Online: Modeling of mouse experiments suggests that optimal anti-hormonal treatment for breast cancer is diet-dependent

Akman, Tuğba / Arendt, Lisa M. / Geisler, Jürgen / Kristensen, Vessela N. / Frigessi, Arnoldo / Köhn-Luque, Alvaro

2023

Abstract	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. Comment: 47 pages, 22 figures
Keywords	Mathematics - Optimization and Control
Subject code	616
Publishing date	2023-01-25
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: An integrated omics approach highlights how epigenetic events can explain and predict response to neoadjuvant chemotherapy and bevacizumab in breast cancer.

Fleischer, Thomas / Haugen, Mads Haugland / Ankill, Jørgen / Silwal-Pandit, Laxmi / Børresen-Dale, Anne-Lise / Hedenfalk, Ingrid / Hatschek, Thomas / Tost, Jörg / Engebraaten, Olav / Kristensen, Vessela N

Molecular oncology

2024

Abstract: Treatment with the anti-angiogenic drug bevacizumab in addition to chemotherapy has shown efficacy for breast cancer in some clinical trials, but better biomarkers are needed to optimally select patients for treatment. Here, we present an omics approach ... ...

Abstract	Treatment with the anti-angiogenic drug bevacizumab in addition to chemotherapy has shown efficacy for breast cancer in some clinical trials, but better biomarkers are needed to optimally select patients for treatment. Here, we present an omics approach where DNA methylation profiles are integrated with gene expression and results from proteomic data in breast cancer patients to predict response to therapy and pinpoint response-related epigenetic events. Fresh-frozen tumor biopsies taken before, during, and after treatment from human epidermal growth factor receptor 2 negative non-metastatic patients receiving neoadjuvant chemotherapy with or without bevacizumab were subjected to molecular profiling. Here, we report that DNA methylation at enhancer CpGs related to cell cycle regulation can predict response to chemotherapy and bevacizumab for the estrogen receptor positive subset of patients (AUC = 0.874), and we validated this observation in an independent patient cohort with a similar treatment regimen (AUC = 0.762). Combining the DNA methylation scores with the scores from a previously published protein signature resulted in a slight increase in the prediction performance (AUC = 0.784). We also show that tumors receiving the combination treatment underwent more extensive epigenetic alterations. Finally, we performed an integrative expression-methylation quantitative trait loci analysis on alterations in DNA methylation and gene expression levels, showing that the epigenetic alterations that occur during treatment are different between responders and non-responders and that these differences may be explained by the proliferation-epithelial-to-mesenchymal transition axis through the activity of grainyhead like transcription factor 2. Using tumor purity computed from copy number data, we developed a method for estimating cancer cell-specific methylation to confirm that the association to response reflects DNA methylation in cancer cells. Taken together, these results support the potential for clinical benefit of the addition of bevacizumab to chemotherapy when administered to the correct patients.
Language	English
Publishing date	2024-04-26
Publishing country	United States
Document type	Journal Article
ZDB-ID	2415106-3
ISSN	1878-0261 ; 1574-7891
ISSN (online)	1878-0261
ISSN	1574-7891
DOI	10.1002/1878-0261.13656
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Article ; Online: Benign breast tumors may arise on different immunological backgrounds.

Torland, Lilly Anne / Lai, Xiaoran / Kumar, Surendra / Riis, Margit H / Geisler, Jürgen / Lüders, Torben / Tekpli, Xavier / Kristensen, Vessela / Sahlberg, Kristine / Tahiri, Andliena

Molecular oncology

2024

Abstract: Benign breast tumors are a nonthreatening condition defined as abnormal cell growth within the breast without the ability to invade nearby tissue. However, benign lesions hold valuable biological information that can lead us toward better understanding ... ...

Abstract	Benign breast tumors are a nonthreatening condition defined as abnormal cell growth within the breast without the ability to invade nearby tissue. However, benign lesions hold valuable biological information that can lead us toward better understanding of tumor biology. In this study, we have used two pathway analysis algorithms, Pathifier and gene set variation analysis (GSVA), to identify biological differences between normal breast tissue, benign tumors and malignant tumors in our clinical dataset. Our results revealed that one-third of all pathways that were significantly different between benign and malignant tumors were immune-related pathways, and 227 of them were validated by both methods and in the METABRIC dataset. Furthermore, five of these pathways (all including genes involved in cytokine and interferon signaling) were related to overall survival in cancer patients in both datasets. The cellular moieties that contribute to immune differences in malignant and benign tumors were analyzed using the deconvolution tool, CIBERSORT. The results showed that levels of some immune cells were specifically higher in benign than in malignant tumors, and this was especially the case for resting dendritic cells and follicular T-helper cells. Understanding the distinct immune profiles of benign and malignant breast tumors may aid in developing noninvasive diagnostic methods to differentiate between them in the future.
Language	English
Publishing date	2024-05-16
Publishing country	United States
Document type	Journal Article
ZDB-ID	2415106-3
ISSN	1878-0261 ; 1574-7891
ISSN (online)	1878-0261
ISSN	1574-7891
DOI	10.1002/1878-0261.13655
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Article ; Online: Spatial transcriptomics inferred from pathology whole-slide images links tumor heterogeneity to survival in breast and lung cancer.

Levy-Jurgenson, Alona / Tekpli, Xavier / Kristensen, Vessela N / Yakhini, Zohar

Scientific reports

2020 Volume 10, Issue 1, Page(s) 18802

Abstract: Digital analysis of pathology whole-slide images is fast becoming a game changer in cancer diagnosis and treatment. Specifically, deep learning methods have shown great potential to support pathology analysis, with recent studies identifying molecular ... ...

Abstract	Digital analysis of pathology whole-slide images is fast becoming a game changer in cancer diagnosis and treatment. Specifically, deep learning methods have shown great potential to support pathology analysis, with recent studies identifying molecular traits that were not previously recognized in pathology H&E whole-slide images. Simultaneous to these developments, it is becoming increasingly evident that tumor heterogeneity is an important determinant of cancer prognosis and susceptibility to treatment, and should therefore play a role in the evolving practices of matching treatment protocols to patients. State of the art diagnostic procedures, however, do not provide automated methods for characterizing and/or quantifying tumor heterogeneity, certainly not in a spatial context. Further, existing methods for analyzing pathology whole-slide images from bulk measurements require many training samples and complex pipelines. Our work addresses these two challenges. First, we train deep learning models to spatially resolve bulk mRNA and miRNA expression levels on pathology whole-slide images (WSIs). Our models reach up to 0.95 AUC on held-out test sets from two cancer cohorts using a simple training pipeline and a small number of training samples. Using the inferred gene expression levels, we further develop a method to spatially characterize tumor heterogeneity. Specifically, we produce tumor molecular cartographies and heterogeneity maps of WSIs and formulate a heterogeneity index (HTI) that quantifies the level of heterogeneity within these maps. Applying our methods to breast and lung cancer slides, we show a significant statistical link between heterogeneity and survival. Our methods potentially open a new and accessible approach to investigating tumor heterogeneity and other spatial molecular properties and their link to clinical characteristics, including treatment susceptibility and survival.
MeSH term(s)	Breast Neoplasms/diagnostic imaging ; Breast Neoplasms/genetics ; Breast Neoplasms/mortality ; Deep Learning ; Female ; Gene Expression ; Genetic Heterogeneity ; Humans ; Image Processing, Computer-Assisted/methods ; Lung Neoplasms/diagnostic imaging ; Lung Neoplasms/genetics ; Lung Neoplasms/mortality ; Male ; MicroRNAs/genetics ; MicroRNAs/metabolism ; RNA, Messenger/genetics ; RNA, Messenger/metabolism ; Survival Rate
Chemical Substances	MicroRNAs ; RNA, Messenger
Language	English
Publishing date	2020-11-02
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-020-75708-z
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