eRapport

The epigenetic landscape of breast cancer: application for prognostic and predictive biomarkers

Prosjekt
Prosjektnummer
2017065
Ansvarlig person
Thomas Fleischer
Institusjon
Oslo universitetssykehus HF
Prosjektkategori
Forskerstipend
Helsekategori
Cancer
Forskningsaktivitet
2. Aetiology
Rapporter
2022 - sluttrapport
Breast cancer is a heterogeneous disease, and prognosis and response to treatments will vary greatly from patient to patient. Analyses of cellular and molecular characteristics allow subgrouping of patients, indicating which therapy the patient should receive and the biological mechanisms driving pathogenesis. The most important subdivision of breast cancers is the over expression of estrogen receptor (ER). In tumors over-expressing ER, estrogen binds to the ER, which further activates genes that promote cell proliferation and tumor growth. In ER negative tumors cell proliferation is driven by other mechanisms such as alterations to the DNA. The importance of epigenetic alterations in both ER positive and ER negative is more uncertain. During this project we have developed a statistical and bioinformatic approach that is able to identify biological functions that are altered in cancer and under epigenetic control. We have shown that estrogen signaling is controlled by the epigenetic landscape of ER positive tumors (Fleischer, Tekpli et al. 2017), and we show that proliferation is epigenetically controlled in also in ER negative tumors (Ankill et al. 2022). These discoveries are important because they give insight into the mechanisms of tumor formation, and these alterations may also be targeted in cancer treatment. Following the results from breast cancer, we are now applying the method across all cancer types. Using data from more than 30 cancer types, we observe that proliferation is linked to loss of methylation across most cancer types, and that the loss of methylation occurs in regulatory regions supporting a causal role for epigenetic alterations. This observation may profoundly alter our understanding of epigenetics in cancer. In support of this we also observe that the identified DNA methylation profiles can predict survival of patients (Ankill et al., manuscript in preparation). To investigate the functional importance of the alterations we observe with the statistical and bioinformatic approach (above), we use CRISPR epigenetic editing to alter specific parts of the epigenome. We have established a system in the lab that allows us to first alter the methylation at specific target loci, then measure alterations in transcription factor binding and target gene expression. The results (still in preparation) will shed further light in the causal role of DNA methylation in regulation of gene expression and tumor development. To predict and explain response to therapy, we have studied two clinical trials (NeoAva and EpiTax), and we show the DNA methylation can serve as a powerful biomarker for predicting response and survival after neo-adjuvant treatment. The NeoAva trial is designed to test whether targeted treatment that stops blood vessel growth to tumors (bevacizumab) improves response to chemotherapy. By using machine learning approaches and both gene expression and DNA methylation data, we can predict which patients will benefit from this drug (Fleischer et al. 2022). In the EpiTax clinical trial, we show that the changes in DNA methylation that occurs during treatment can be used to predict long term survival of patients (Pedersen et al. 2022). In collaboration with Department of Biostatistics (UiO), we have developed a Bayesian statistics framework to perform clustering of samples using RNA-seq from cancer patient samples (Eliseussen et al. 2022), and we have applied this approach on tumors from cancers originating from the whole body (Vitelli, Fleischer et al. 2022). In this work, we show that the majority of samples cluster according to tissue of origin, and we also identify three pan-squamous clusters of head and neck carcinoma, lung squamous carcinoma and bladder cancer. Additionally, in collaboration with Tero Aittokallio we have applied several machine learning approaches, and we're able to predict invasiveness of tumors (Xu et al. 2021). In this project we have contributed to the understanding of how epigenetics affects cancer-driving pathways, and importantly, this information can be used to for improved biomarker discovery (Xu et al. 2021; Fleischer et al. 2022). Modern biomarker discovery often uses artificial intelligence (AI) or machine learning for analysis of 'omics data; however, a problem with this approach is that AI and machine learning is vulnerable to the excessive amount of genomic features present in 'omics data. An effective remedy for this problem is to use biological knowledge to pre-select important features from 'omics data, such as important genes or important methylated sites (CpGs). Our results from the NeoAva clinical trial highlights this advantage, as pre-selection of CpGs results in very high accuracy when performing machine learning for predicting response to chemotherapy and the anti-angiogenic drug bevacizumab. Bevacizumab is a monoclonal antibody that binds to vascular endothelial growth factor (VEGF) and blocks growth of blood vessels. Although the drug has shown efficacy in some trials, it did not increase overall survival. In the NeoAva trial, we have shown that Bevacizumab can improve response to neoadjuvant chemotherapy in patients with ER positive tumors (Silwal-Pandit et al. 2017). There is an urgent need for the introduction of novel biomarkers, and a better understanding of the biological mechanisms leading to response or resistance to this treatment. The aim of this study was to use DNA methylation to predict response to treatment. We show that DNA methylation related to cell cycle regulation can predict response to chemotherapy and bevacizumab for the ER positive subset of patients with high accuracy, and we can validate this observation in an independent patient cohort with a similar treatment. Finally, improved predictive biomarkers will allow more drugs (e.g. bevacizumab) to be used on individual patients groups. This in turn, will improve life expectancy of patients, and it will also save money as we will not administer expensive drugs to patients who will not benefit from the treatment. Importantly, this will also reduce the burden of side effects for patients who will not benefit from the treatment.

Nei

2021
We have identified potential epigenetic cancer-driving epigenetic alterations breast tumors, and we have developed and applied machine learning algorithms to predict invasiveness of tumors and prognosis of patients. We are now using CRISPR epigenetic editing to reverse these epigenetic changes to improve therapy.Breast cancer is a heterogeneous disease and can be viewed as a collection of different diseases, and prognosis and response to treatments will vary greatly from patient to patient. Analyses of cellular and molecular characteristics allow subgrouping of patients, indicating which therapy the patient should receive and the biological mechanisms driving pathogenesis. The most important subdivision of breast cancers is the over expression of the protein estrogen receptor (ER). In tumors over-expressing ER, the hormone estrogen binds to the ER, which further activates genes that promote cell proliferation and tumor growth. In ER negative tumors cell proliferation is driven by other mechanisms such as alterations to the DNA, and the importance of epigenetic alterations is more uncertain. We have developed a statistical and bioinformatic approach that is able to identify biological functions that are altered in cancer and under epigenetic control. We have shown that estrogen signaling is controlled by the epigenetic landscape of ER positive tumors (Fleischer et al., 2017), and we show that proliferation is epigenetically controlled in also in ER negative tumors (Ankill et al., bioRxiv 2021; under review in NAR Cancer). These discoveries are important because they give insight into how normal cells become tumor cells, and these alterations may also be targeted in cancer treatment. To investigate the functional importance of the alterations we observe with the statistical and bioinformatic approach (above), we use CRISPR epigenetic editing to alter specific parts of the epigenome. This allows us to measure the effects on proliferation, tumor grow and resistance to therapy after epigenetic alteration (ongoing labwork and manuscript preparation). We are also working on prediction of therapy response in the NeoAva clinical trial. This trial is designed to test whether targeted treatment that stops blood vessel growth to tumors (bevacizumab) improves response to chemotherapy. We have previously showed that the response to treatment is improved in patients with ER positive tumors (Silwal-Pandit et al., 2017), and now, by using machine learning approaches and both gene expression and DNA methylation data in combination with a protein signature (Haugen et al. 2020), we can predict which patients will benefit from this drug (Fleischer et al., soon to be submitted). In collaboration with Department of Biostatistics (UiO), we are using advanced (Bayesian) statistics to identify groups of tumors when looking at cancers originating from the whole body (pan-cancer analysis; Vitelli, Fleischer et al., soon to be submitted), and together with Tero Aittokallio we have applied several machine learning approaches to predict invasiveness of tumors (Xu et al., Front Genet 2021).

nei

2020
We have developed a method that allows identification of epigenetically regulated pathways in breast cancer, and we have identified potential drivers of both estrogen receptor positive and negative tumors. We are now using CRISPR epigenetic editing to reverse these epigenetic changes to improve therapy.Breast cancer is a heterogeneous disease and can be viewed as a collection of different diseases, and prognosis and response to treatments will vary greatly from patient to patient. Analyses of cellular and molecular characteristics allow subgrouping of patients, indicating which therapy the patient should receive and the biological mechanisms driving pathogenesis. The most important subdivision of breast cancers is the over expression of the protein estrogen receptor (ER). In tumors over-expressing ER, the hormone estrogen binds to the ER, which further activates genes that promote cell proliferation and tumor growth. In ER negative tumors cell proliferation is driven by other mechanisms such as alterations to the DNA, and the importance of epigenetic alterations is more uncertain. We have developed a statistical and bioinformatic approach that is able to identify biological functions that are altered in cancer and under epigenetic control. We have shown that estrogen signaling is controlled by the epigenetic landscape of ER positive tumors (Fleischer et al., 2017), and we are now preparing a manuscript where we show that proliferation is epigenetically controlled in ER negative tumors (Ankill et al., soon to be submitted). These discoveries are important because they give insight into how normal cells become tumor cells, and these alterations may also be targeted in cancer treatment. To investigate the functional importance of the alterations we observe with the statistical and bioinformatic approach (above), we use CRISPR epigenetic editing to alter specific parts of the epigenome. This allows us to measure the effects on proliferation, tumor grow and resistance to therapy after epigenetic alteration (ongoing labwork and manuscript preparation). We are also working on prediction of therapy response in the NeoAva clinical trial. This trial is designed to test whether targeted treatment that stops blood vessel growth to tumors (bevacizumab) improves response to chemotherapy. We have previously showed that the response to treatment is improved in patients with ER positive tumors (Silwal-Pandit et al., 2017), and now, by using machine learning approaches and both gene expression and DNA methylation data, we can predict which patients will receive benefit from this drug (Fleischer et al., soon to be submitted). In collaboration with Department of Biostatistics (UiO), we are using advanced (Bayesian) statistics to identify groups of tumors when looking at cancers originating from the whole body (pan-cancer analysis). We are discovering that certain cancers with squamous characteristics belong to groups independent of which organ it arose in, and that we can subdivide other cancers into meaningful groups with clinical importance. We also see that this clustering method is exceptionally stable and identifies differences that traditional methods would not find (Vitelli, Fleischer et al., soon to be submitted).

Nei

2019
We have developed a method that allows identification of epigenetically regulated pathways in breast cancer, and we have shown that DNA methylation and enhancers can distinguish and explain estrogen receptor positive breast cancer. We have also identified biomarkers of prognosis and treatment response contributing to personalized cancer treatment.Breast cancer is a heterogeneous disease and can be viewed as a collection of different diseases, and prognosis and response to treatments will vary greatly from patient to patient. Analyses of cellular and molecular characteristics allow subgrouping of patients, indicating which therapy the patient should receive and the biological mechanisms driving pathogenesis. The most important subdivision of breast cancers is the over expression of the protein estrogen receptor (ER). In tumors over-expressing ER, the hormone estrogen binds to the ER, which further activates genes that promote cell proliferation and tumor growth. ER is a transcription factor: a protein that can bind to the DNA and activate the expression of other genes. Regulation of gene expression by transcription factors often happens when transcription factors bind to enhancers, which are regulatory regions in the DNA located in the relatively far away from a gene. We developed a new statistical approach that identified enhancers and binding regions of ER where DNA methylation was lost in ER positive breast cancers compared to healthy controls, which allows the activation of genes which fuel the pathological growth and proliferation of ER positive breast cancers (Fleischer, Tekpli, et al., 2017). Patients with ER positive breast cancer have a relatively good prognosis partly due to anti-estrogen treatments such as Tamoxifen and aromatase inhibitors. However, many ER positive tumors may be aggressive, and a big clinical challenge is selecting which of these patients should also get chemotherapy. We have developed a biomarker based on DNA methylation that may further subdivide these patients into subgroups with different prognosis, potentially contributing to improved personalized therapy (Fleischer, Klajic, et al., 2017). The NeoAva clinical trial is designed to test whether targeted treatment that stops blood vessel growth to tumors (bevacizumab) improves response to chemotherapy. We showed that the response to treatment is improved in patients with ER positive tumors (Silwal-Pandit et al., 2017), and also that patients with more alterations in the cancer genome respond better to the treatment in general (Hoglander et al., 2018). The levels of signaling molecules (cytokines) used by the immune system were also measured, and lower levels of many cytokines were found in the patients treated with bevacizumab (Jabeen et al., 2018). Recently, we have developed mathematical models that simulate the life and death of individual cancer cells in patients. The models utilize patient and tumor “portraits” that include molecular and clinical information. With this, we are able to predict the response to therapy for individual patients and to suggest and test other treatment regimens (Lai et al., 2019) Ongoing work focuses on developing DNA methylation based biomarkers that further help selecting patients for specific treatments, and also to understand the biological changes that occur when treating tumors with chemotherapy or bevacizumab. To improve our ability to understand changes in tumor cells, we are developing a method to estimate the specific signal originating from tumor cells. Also, we are studying the functional effect of altered enhancer methylation using CRISPR precision epigenetic engineering in cancer cells.

NEI

2018
We have shown that loss of DNA methylation at enhancers is a key feature of estrogen receptor (ER) positive breast cancer (Fleischer, Tekpli et al. Nat Commun 2017). Now, we identify alterations in DNA methylation related to EMT in ER positive breast cancer, and we identify causal alterations using epigenetic editing (CRISPR dCas9-DNMT).The most important subdivision of breast cancers is the over expression of the transcription factor estrogen receptor (ER). In tumors over expressing ER, the hormone estrogen binds to the ER, which further activates genes that promote cell proliferation and tumor growth. Regulation of gene expression by transcription factors often happens when transcription factors bind to enhancers. DNA methylation is the addition of a methyl group (CH3-) to the cytosine base in the DNA, and this modification is thought to hinder the binding of proteins to DNA resulting in decreased expression of neighboring genes. We developed a new statistical approach that identify enhancers and binding regions of transcription factors that are differentially methylated between patient groups. With this approach we have already shown that loss of DNA methylation at enhancers is a key feature of estrogen receptor (ER) positive breast cancer (Fleischer, Tekpli et al. Nat Commun 2017). The project is currently focusing on ER positive breast cancer and has three main aims: 1) identify enhancers differentially methylated between ER positive breast tumors that regulate biological functions, 2) to identify enhancer methylation associated to response to therapy for patients treated with chemotherapy +/- bevacizumab, and 3) to assess the causal role of DNA methylation alterations in ER positive tumors using epigenetic editing with CRISPR dCas9-DNMT3A. 1) Using the approach that we reported in Fleischer, Tekpli et al., Nat Commun 2017 on ER positive tumors only, we identify CpGs and genes associated to epithelial to mesenchymal transition (EMT). The patients were scored for EMT using a gene expression derived signature, and we show that the methylation level of the identified CpGs was associated with the EMT score. The CpGs are found in enhancers and in binding regions of transcription factors such as TWIST1, TEAD1 and YAP1, all known to be involved in EMT. Taken together, we show that epigenetic alterations may play a key role in EMT (and hence invasiveness) of ER positive breast cancers, and this may have great implications for our understanding of ER positive breast pathogenesis. A manuscript in under preparation. 2) Also in this sub-project we use a variation of the same method, and we look at changes in DNA methylation and gene expression during neo-adjuvant treatment with chemotherapy +/- bevacizumab. We observe methylation changes in CpGs found in transcription factor binding regions of transcription factors known to be involved in chromatin structure such as the polycomb repressor complex, and that the variations in methylation changes are associated to response to therapy. This observation suggests that epigenetic regulation of the polycomb repressor complex may be involved in response to therapy, and this will be validated in other clinical trials. 3) We want to assess the causal role of epigenetic alterations in ER positive breast cancer using epigenetic editing. We use the ER positive breast cancer cell line MCF7 and incorporate a deactivated Cas9 (dCas9) fused with the catalytic domain of the DNA methyltransferase DNMT3A. When these cells are transfected with guide RNAs, we have shown that we are able to methylate specific enhancers involved in estrogen signaling. Ongoing studies include assessment of gene expression changes caused by the altered DNA methylation, as well as proliferations and migration assays of the altered cells.

Nei

2017
In a recently published article we find that DNA methylation at enhancers and transcription factor binding regions is a key feature distinguishing and explaining estrogen receptor positive breast cancer. The method we used is transferable to other cancers, cancer subtypes, and cancer treatment situations.Breast cancer is a heterogeneous disease and can be viewed as a collection of different diseases, and tumors from patient to patient can be very different. Analyses of cellular and molecular characteristics allow subgrouping of patients, indicating which therapy the patient should receive and the biological mechanisms driving the pathogenesis. The most important subdivision of breast cancers is the over expression of the protein “estrogen receptor”. In tumors over expressing estrogen receptor, the hormone estrogen binds to the estrogen receptor, which further activates genes that promote cell proliferation and tumor growth. The estrogen receptor is a transcription factor: a protein that can bind to the DNA and activate the expression of other genes. Regulation of gene expression by transcription factors often happens when transcription factors bind to enhancers; enhancers are regulatory regions in the DNA located in the vicinity of a gene. DNA methylation is the addition of a methyl group (CH3-) to the cytosine base in the DNA, and this modification changes the properties of the DNA molecule. It is thought that DNA methylation could hinder the binding of proteins to DNA, resulting in decreased expression of neighboring genes. In this way, DNA methylation may regulate which genes are active in different tissues and tumors. We developed a new statistical approach which identified enhancers and binding regions of estrogen receptor where DNA methylation was lower in estrogen receptor positive breast cancers compared to healthy controls. We highlight the abnormal low methylation of specific enhancers in estrogen receptor positive breast cancer that allows the activation of genes which fuel the pathological growth and proliferation of estrogen receptor positive breast cancers. The study provides evidence of how estrogen receptor positive breast cancers become abnormal at the molecular level, and also how they are different from the estrogen receptor negative breast cancers. This is an important insight for understanding the abnormal biology occurring during breast cancer development. In future work we will apply this method to cancer patients that are treated with chemotherapy and an antiangiogenic drug called Bevacizumab, and study how transcription factors and DNA methylation may be involved with treatment response and resistance. Also we will study differences within the ER positive subgroup to identify biological and clinical differences.
Vitenskapelige artikler
Ankill J, Aure MR, Bjørklund S, Langberg S, , Kristensen VN, Vitelli V, Tekpli X, Fleischer T

Epigenetic alterations at distal enhancers are linked to proliferation in human breast cancer.

NAR Cancer 2022 Mar;4(1):zcac008. Epub 2022 mar 24

PMID: 35350772

Bjørklund SS, Aure MR, Häkkinen J, Vallon-Christersson J, Kumar S, Evensen KB, Fleischer T, Tost J, , Sahlberg KK, Mathelier A, Bhanot G, Ganesan S, Tekpli X, Kristensen VN

Subtype and cell type specific expression of lncRNAs provide insight into breast cancer.

Commun Biol 2022 Aug 18;5(1):834. Epub 2022 aug 18

PMID: 35982125

Eliseussen E, Fleischer T, Vitelli V

Rank-based Bayesian variable selection for genome-wide transcriptomic analyses.

Stat Med 2022 Oct 15;41(23):4532. Epub 2022 jul 18

PMID: 35844145

Lemma RB, Fleischer T, Martinsen E, Ledsaak M, Kristensen V, Eskeland R, Gabrielsen OS, Mathelier A

Pioneer transcription factors are associated with the modulation of DNA methylation patterns across cancers.

Epigenetics Chromatin 2022 Apr 19;15(1):13. Epub 2022 apr 19

PMID: 35440061

Mo T, Brandal SHB, Köhn-Luque A, Engebraaten O, Kristensen VN, Fleischer T, Hompland T, Seierstad T

Quantification of Tumor Hypoxia through Unsupervised Modelling of Consumption and Supply Hypoxia MR Imaging in Breast Cancer.

Cancers (Basel) 2022 Mar 04;14(5). Epub 2022 mar 4

PMID: 35267636

Pedersen CA, Cao MD, Fleischer T, Rye MB, Knappskog S, Eikesdal HP, Lønning PE, Tost J, Kristensen VN, Tessem MB, Giskeødegård GF, Bathen TF

DNA methylation changes in response to neoadjuvant chemotherapy are associated with breast cancer survival.

Breast Cancer Res 2022 Jun 24;24(1):43. Epub 2022 jun 24

PMID: 35751095

Vitelli V, Fleischer T, Ankill J, Arjas E, Frigessi A, Kristensen VN, Zucknick M

Transcriptomic pan-cancer analysis using rank-based Bayesian inference.

Mol Oncol 2023 Apr;17(4):548. Epub 2023 jan 23

PMID: 36562628

Aure MR, Fleischer T, Bjørklund S, Ankill J, Castro-Mondragon JA, , Børresen-Dale AL, Tost J, Sahlberg KK, Mathelier A, Tekpli X, Kristensen VN

Crosstalk between microRNA expression and DNA methylation drives the hormone-dependent phenotype of breast cancer.

Genome Med 2021 04 29;13(1):72. Epub 2021 apr 29

PMID: 33926515

Jaiswal A, Gautam P, Pietilä EA, Timonen S, Nordström N, Akimov Y, Sipari N, Tanoli Z, Fleischer T, Lehti K, Wennerberg K, Aittokallio T

Multi-modal meta-analysis of cancer cell line omics profiles identifies ECHDC1 as a novel breast tumor suppressor.

Mol Syst Biol 2021 03;17(3):e9526.

PMID: 33750001

Xu H, Lien T, Bergholtz H, Fleischer T, Djerroudi L, Vincent-Salomon A, Sørlie T, Aittokallio T

Multi-Omics Marker Analysis Enables Early Prediction of Breast Tumor Progression.

Front Genet 2021;12():670749. Epub 2021 jun 3

PMID: 34149812

Lai X, Geier OM, Fleischer T, Garred Ø, Borgen E, Funke SW, Kumar S, Rognes ME, Seierstad T, Børresen-Dale AL, Kristensen VN, Engebraaten O, Köhn-Luque A, Frigessi A

Toward Personalized Computer Simulation of Breast Cancer Treatment: A Multiscale Pharmacokinetic and Pharmacodynamic Model Informed by Multitype Patient Data.

Cancer Res 2019 Aug 15;79(16):4293-4304. Epub 2019 mai 22

PMID: 31118201

Höglander EK, Nord S, Wedge DC, Lingjærde OC, Silwal-Pandit L, Gythfeldt HV, Vollan HKM, Fleischer T, Krohn M, Schlitchting E, Borgen E, Garred Ø, Holmen MM, Wist E, Naume B, Van Loo P, Børresen-Dale AL, Engebraaten O, Kristensen V

Time series analysis of neoadjuvant chemotherapy and bevacizumab-treated breast carcinomas reveals a systemic shift in genomic aberrations.

Genome Med 2018 11 29;10(1):92. Epub 2018 nov 29

PMID: 30497530

Jabeen S, Zucknick M, Nome M, Dannenfelser R, Fleischer T, Kumar S, Lüders T, von der Lippe Gythfeldt H, Troyanskaya O, Kyte JA, Børresen-Dale AL, Naume B, Tekpli X, Engebraaten O, Kristensen V

Serum cytokine levels in breast cancer patients during neoadjuvant treatment with bevacizumab.

Oncoimmunology 2018;7(11):e1457598. Epub 2018 aug 6

PMID: 30377556

Fleischer T, Tekpli X, Mathelier A, Wang S, Nebdal D, Dhakal HP, Sahlberg KK, Schlichting E, , Børresen-Dale AL, Borgen E, Naume B, Eskeland R, Frigessi A, Tost J, Hurtado A, Kristensen VN

DNA methylation at enhancers identifies distinct breast cancer lineages.

Nat Commun 2017 Nov 09;8(1):1379. Epub 2017 nov 9

PMID: 29123100

Fleischer T, Klajic J, Aure MR, Louhimo R, Pladsen AV, Ottestad L, Touleimat N, Laakso M, Halvorsen AR, Grenaker Alnæs GI, Riis ML, Helland Å, Hautaniemi S, Lønning PE, Naume B, Børresen-Dale AL, Tost J, Kristensen VN

DNA methylation signature (SAM40) identifies subgroups of the Luminal A breast cancer samples with distinct survival.

Oncotarget 2017 Jan 03;8(1):1074-1082.

PMID: 27911866

Silwal-Pandit L, Nord S, von der Lippe Gythfeldt H, Møller EK, Fleischer T, Rødland E, Krohn M, Borgen E, Garred Ø, Olsen T, Vu P, Skjerven H, Fangberget A, Holmen MM, Schlitchting E, Wille E, Nordberg Stokke M, Moen Vollan HK, Kristensen V, Langerød A, Lundgren S, Wist E, Naume B, Lingjærde OC, Børresen-Dale AL, Engebraaten O

The Longitudinal Transcriptional Response to Neoadjuvant Chemotherapy with and without Bevacizumab in Breast Cancer.

Clin Cancer Res 2017 Aug 15;23(16):4662-4670. Epub 2017 mai 9

PMID: 28487444

Fleischer T, Haugen MH, Ankill j, Silwal-Pandit l, Børresen-Dale A-L, Hedenfalk I, Hatschek T, Tost J, Engebraaten O, Kristensen VN

An integrated ‘omics approach highlights the role of epigenetic events to explain and predict response to neoadjuvant chemotherapy and bevacizumab

bioRxiv, 2022

Ankill J, Aure MR, Bjørklund S, Langberg S, Oslo Breast Cancer Research Consortium (OSBREAC), Kristensen VN, Vitelli V, Tekpli X, Fleischer T

Epigenetic alterations at distal enhancers are linked to proliferation in human breast cancer

bioRxiv, 2021

Deltagere
  • Manuela Karola Zucknick Forsker (annen finansiering)
  • Valeria Vitelli Forsker (annen finansiering)
  • Brock Christensen Prosjektdeltaker
  • Zdenko Herceg Prosjektdeltaker
  • Olga Troyanskaya Prosjektdeltaker
  • Jörg Tost Prosjektdeltaker
  • Antoni Hurtado Rodriguez Prosjektdeltaker
  • Anthony Mathelier Prosjektdeltaker
  • Ragnhild Eskeland Prosjektdeltaker
  • Arnoldo Frigessi Prosjektdeltaker
  • Bjørn Naume Prosjektdeltaker
  • Olav Engebråten Prosjektdeltaker
  • Marie Elise Engkvist Doktorgradsstipendiat (annen finansiering)
  • Jørgen Ankill Prosjektdeltaker
  • Vessela N. Kristensen Forskningsgruppeleder
  • Thomas Fleischer Forsker (annen finansiering)

eRapport er utarbeidet av Sølvi Lerfald og Reidar Thorstensen, Regionalt kompetansesenter for klinisk forskning, Helse Vest RHF, og videreutvikles av de fire RHF-ene i fellesskap, med støtte fra Helse Vest IKT

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