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1. Integrative pan-cancer analysis reveals a common architecture of dysregulated transcriptional networks characterized by loss of enhancer methylation.

Author

Jørgen Ankill, Zhi Zhao, Xavier Tekpli, Elin H Kure, Vessela N Kristensen, Anthony Mathelier, and Thomas Fleischer

Subjects
Biology (General), QH301-705.5
Abstract

Aberrant DNA methylation contributes to gene expression deregulation in cancer. However, these alterations' precise regulatory role and clinical implications are still not fully understood. In this study, we performed expression-methylation Quantitative Trait Loci (emQTL) analysis to identify deregulated cancer-driving transcriptional networks linked to CpG demethylation pan-cancer. By analyzing 33 cancer types from The Cancer Genome Atlas, we identified and confirmed significant correlations between CpG methylation and gene expression (emQTL) in cis and trans, both across and within cancer types. Bipartite network analysis of the emQTL revealed groups of CpGs and genes related to important biological processes involved in carcinogenesis including proliferation, metabolism and hormone-signaling. These bipartite communities were characterized by loss of enhancer methylation in specific transcription factor binding regions (TFBRs) and the CpGs were topologically linked to upregulated genes through chromatin loops. Penalized Cox regression analysis showed a significant prognostic impact of the pan-cancer emQTL in many cancer types. Taken together, our integrative pan-cancer analysis reveals a common architecture where hallmark cancer-driving functions are affected by the loss of enhancer methylation and may be epigenetically regulated.

Published
2024
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2. High-throughput screen in vitro identifies dasatinib as a candidate for combinatorial treatment with HER2-targeting drugs in breast cancer.

Author

Lisa Svartdal Normann, Mads Haugland Haugen, Vesa Hongisto, Miriam Ragle Aure, Suvi-Katri Leivonen, Vessela N Kristensen, Andliena Tahiri, Olav Engebraaten, Kristine Kleivi Sahlberg, and Gunhild Mari Mælandsmo

Subjects
Medicine, Science
Abstract

Human epidermal growth factor receptor 2-positive (HER2+) breast cancer is an aggressive subtype of this disease. Targeted treatment has improved outcome, but there is still a need for new therapeutic strategies as some patients respond poorly to treatment. Our aim was to identify compounds that substantially affect viability in HER2+ breast cancer cells in response to combinatorial treatment. We performed a high-throughput drug screen of 278 compounds in combination with trastuzumab and lapatinib using two HER2+ breast cancer cell lines (KPL4 and SUM190PT). The most promising drugs were validated in vitro and in vivo, and downstream molecular changes of the treatments were analyzed. The screen revealed multiple drugs that could be used in combination with lapatinib and/or trastuzumab. The Src-inhibitor dasatinib showed the largest combinatorial effect together with lapatinib in the KPL4 cell line compared to treatment with dasatinib alone (p < 0.01). In vivo, only lapatinib significantly reduced tumor growth (p < 0.05), whereas dasatinib alone, or in combination with lapatinib, did not show significant effects. Protein analyses of the treated xenografts showed significant alterations in protein levels compared to untreated controls, suggesting that all drugs reached the tumor and exerted a measurable effect. In silico analyses suggested activation of apoptosis and reduced activity of survival pathways by all treatments, but the opposite pattern was observed for the combinatorial treatment compared to lapatinib alone.

Published
2023
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3. miRNA normalization enables joint analysis of several datasets to increase sensitivity and to reveal novel miRNAs differentially expressed in breast cancer.

Author

Shay Ben-Elazar, Miriam Ragle Aure, Kristin Jonsdottir, Suvi-Katri Leivonen, Vessela N Kristensen, Emiel A M Janssen, Kristine Kleivi Sahlberg, Ole Christian Lingjærde, and Zohar Yakhini

Subjects
Biology (General), QH301-705.5
Abstract

Different miRNA profiling protocols and technologies introduce differences in the resulting quantitative expression profiles. These include differences in the presence (and measurability) of certain miRNAs. We present and examine a method based on quantile normalization, Adjusted Quantile Normalization (AQuN), to combine miRNA expression data from multiple studies in breast cancer into a single joint dataset for integrative analysis. By pooling multiple datasets, we obtain increased statistical power, surfacing patterns that do not emerge as statistically significant when separately analyzing these datasets. To merge several datasets, as we do here, one needs to overcome both technical and batch differences between these datasets. We compare several approaches for merging and jointly analyzing miRNA datasets. We investigate the statistical confidence for known results and highlight potential new findings that resulted from the joint analysis using AQuN. In particular, we detect several miRNAs to be differentially expressed in estrogen receptor (ER) positive versus ER negative samples. In addition, we identify new potential biomarkers and therapeutic targets for both clinical groups. As a specific example, using the AQuN-derived dataset we detect hsa-miR-193b-5p to have a statistically significant over-expression in the ER positive group, a phenomenon that was not previously reported. Furthermore, as demonstrated by functional assays in breast cancer cell lines, overexpression of hsa-miR-193b-5p in breast cancer cell lines resulted in decreased cell viability in addition to inducing apoptosis. Together, these observations suggest a novel functional role for this miRNA in breast cancer. Packages implementing AQuN are provided for Python and Matlab: https://github.com/YakhiniGroup/PyAQN.

Published
2021
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4. Efficient gene expression signature for a breast cancer immuno-subtype.

Author

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

Subjects
Medicine, Science
Abstract

Motivation and backgroundThe 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.ResultsIn 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.

Published
2021
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5. LIMT is a novel metastasis inhibiting lncRNA suppressed by EGF and downregulated in aggressive breast cancer

Author

Aldema Sas‐Chen, Miriam R Aure, Limor Leibovich, Silvia Carvalho, Yehoshua Enuka, Cindy Körner, Maria Polycarpou‐Schwarz, Sara Lavi, Nava Nevo, Yuri Kuznetsov, Justin Yuan, Francisco Azuaje, Oslo Breast Cancer Research Consortium (OSBREAC), Igor Ulitsky, Sven Diederichs, Stefan Wiemann, Zohar Yakhini, Vessela N Kristensen, Anne‐Lise Børresen‐Dale, Yosef Yarden, Torill Sauer, Jürgen Geisler, Solveig Hofvind, Tone F Bathen, Elin Borgen, Olav Engebråten, Øystein Fodstad, Øystein Garred, Gry Aarum Geitvik, Rolf Kåresen, Bjørn Naume, Gunhild Mari Mælandsmo, Hege G Russnes, Ellen Schlichting, Therese Sørlie, Ole Christian Lingjærde, Kristine Kleivi Sahlberg, Helle Kristine Skjerven, and Britt Fritzman

Subjects
biomarkers, breast cancer, long noncoding RNA, migration, receptor tyrosine kinase, Medicine (General), R5-920, Genetics, QH426-470
Abstract

Abstract Long noncoding RNAs (lncRNAs) are emerging as regulators of gene expression in pathogenesis, including cancer. Recently, lncRNAs have been implicated in progression of specific subtypes of breast cancer. One aggressive, basal‐like subtype associates with increased EGFR signaling, while another, the HER2‐enriched subtype, engages a kin of EGFR. Based on the premise that EGFR‐regulated lncRNAs might control the aggressiveness of basal‐like tumors, we identified multiple EGFR‐inducible lncRNAs in basal‐like normal cells and overlaid them with the transcriptomes of over 3,000 breast cancer patients. This led to the identification of 11 prognostic lncRNAs. Functional analyses of this group uncovered LINC01089 (here renamed LncRNA Inhibiting Metastasis; LIMT), a highly conserved lncRNA, which is depleted in basal‐like and in HER2‐positive tumors, and the low expression of which predicts poor patient prognosis. Interestingly, EGF rapidly downregulates LIMT expression by enhancing histone deacetylation at the respective promoter. We also find that LIMT inhibits extracellular matrix invasion of mammary cells in vitro and tumor metastasis in vivo. In conclusion, lncRNAs dynamically regulated by growth factors might act as novel drivers of cancer progression and serve as prognostic biomarkers.

Published
2016
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6. Canine Mammary Tumours Are Affected by Frequent Copy Number Aberrations, including Amplification of MYC and Loss of PTEN.

Author

Kaja S Borge, Silje Nord, Peter Van Loo, Ole C Lingjærde, Gjermund Gunnes, Grethe I G Alnæs, Hiroko K Solvang, Torben Lüders, Vessela N Kristensen, Anne-Lise Børresen-Dale, and Frode Lingaas

Subjects
Medicine, Science
Abstract

BackgroundCopy number aberrations frequently occur during the development of many cancers. Such events affect dosage of involved genes and may cause further genomic instability and progression of cancer. In this survey, canine SNP microarrays were used to study 117 canine mammary tumours from 69 dogs.ResultsWe found a high occurrence of copy number aberrations in canine mammary tumours, losses being more frequent than gains. Increased frequency of aberrations and loss of heterozygosity were positively correlated with increased malignancy in terms of histopathological diagnosis. One of the most highly recurrently amplified regions harbored the MYC gene. PTEN was located to a frequently lost region and also homozygously deleted in five tumours. Thus, deregulation of these genes due to copy number aberrations appears to be an important event in canine mammary tumour development. Other potential contributors to canine mammary tumour pathogenesis are COL9A3, INPP5A, CYP2E1 and RB1. The present study also shows that a more detailed analysis of chromosomal aberrations associated with histopathological parameters may aid in identifying specific genes associated with canine mammary tumour progression.ConclusionsThe high frequency of copy number aberrations is a prominent feature of canine mammary tumours as seen in other canine and human cancers. Our findings share several features with corresponding studies in human breast tumours and strengthen the dog as a suitable model organism for this disease.

Published
2015
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7. MicroRNA related polymorphisms and breast cancer risk.

Author

Sofia Khan, Dario Greco, Kyriaki Michailidou, Roger L Milne, Taru A Muranen, Tuomas Heikkinen, Kirsimari Aaltonen, Joe Dennis, Manjeet K Bolla, Jianjun Liu, Per Hall, Astrid Irwanto, Keith Humphreys, Jingmei Li, Kamila Czene, Jenny Chang-Claude, Rebecca Hein, Anja Rudolph, Petra Seibold, Dieter Flesch-Janys, Olivia Fletcher, Julian Peto, Isabel dos Santos Silva, Nichola Johnson, Lorna Gibson, Zoe Aitken, John L Hopper, Helen Tsimiklis, Minh Bui, Enes Makalic, Daniel F Schmidt, Melissa C Southey, Carmel Apicella, Jennifer Stone, Quinten Waisfisz, Hanne Meijers-Heijboer, Muriel A Adank, Rob B van der Luijt, Alfons Meindl, Rita K Schmutzler, Bertram Müller-Myhsok, Peter Lichtner, Clare Turnbull, Nazneen Rahman, Stephen J Chanock, David J Hunter, Angela Cox, Simon S Cross, Malcolm W R Reed, Marjanka K Schmidt, Annegien Broeks, Laura J Van't Veer, Frans B Hogervorst, Peter A Fasching, Michael G Schrauder, Arif B Ekici, Matthias W Beckmann, Stig E Bojesen, Børge G Nordestgaard, Sune F Nielsen, Henrik Flyger, Javier Benitez, Pilar M Zamora, Jose I A Perez, Christopher A Haiman, Brian E Henderson, Fredrick Schumacher, Loic Le Marchand, Paul D P Pharoah, Alison M Dunning, Mitul Shah, Robert Luben, Judith Brown, Fergus J Couch, Xianshu Wang, Celine Vachon, Janet E Olson, Diether Lambrechts, Matthieu Moisse, Robert Paridaens, Marie-Rose Christiaens, Pascal Guénel, Thérèse Truong, Pierre Laurent-Puig, Claire Mulot, Frederick Marme, Barbara Burwinkel, Andreas Schneeweiss, Christof Sohn, Elinor J Sawyer, Ian Tomlinson, Michael J Kerin, Nicola Miller, Irene L Andrulis, Julia A Knight, Sandrine Tchatchou, Anna Marie Mulligan, Thilo Dörk, Natalia V Bogdanova, Natalia N Antonenkova, Hoda Anton-Culver, Hatef Darabi, Mikael Eriksson, Montserrat Garcia-Closas, Jonine Figueroa, Jolanta Lissowska, Louise Brinton, Peter Devilee, Robert A E M Tollenaar, Caroline Seynaeve, Christi J van Asperen, Vessela N Kristensen, kConFab Investigators, Australian Ovarian Cancer Study Group, Susan Slager, Amanda E Toland, Christine B Ambrosone, Drakoulis Yannoukakos, Annika Lindblom, Sara Margolin, Paolo Radice, Paolo Peterlongo, Monica Barile, Paolo Mariani, Maartje J Hooning, John W M Martens, J Margriet Collée, Agnes Jager, Anna Jakubowska, Jan Lubinski, Katarzyna Jaworska-Bieniek, Katarzyna Durda, Graham G Giles, Catriona McLean, Hiltrud Brauch, Thomas Brüning, Yon-Dschun Ko, GENICA Network, Hermann Brenner, Aida Karina Dieffenbach, Volker Arndt, Christa Stegmaier, Anthony Swerdlow, Alan Ashworth, Nick Orr, Michael Jones, Jacques Simard, Mark S Goldberg, France Labrèche, Martine Dumont, Robert Winqvist, Katri Pylkäs, Arja Jukkola-Vuorinen, Mervi Grip, Vesa Kataja, Veli-Matti Kosma, Jaana M Hartikainen, Arto Mannermaa, Ute Hamann, Georgia Chenevix-Trench, Carl Blomqvist, Kristiina Aittomäki, Douglas F Easton, and Heli Nevanlinna

Subjects
Medicine, Science
Abstract

Genetic variations, such as single nucleotide polymorphisms (SNPs) in microRNAs (miRNA) or in the miRNA binding sites may affect the miRNA dependent gene expression regulation, which has been implicated in various cancers, including breast cancer, and may alter individual susceptibility to cancer. We investigated associations between miRNA related SNPs and breast cancer risk. First we evaluated 2,196 SNPs in a case-control study combining nine genome wide association studies (GWAS). Second, we further investigated 42 SNPs with suggestive evidence for association using 41,785 cases and 41,880 controls from 41 studies included in the Breast Cancer Association Consortium (BCAC). Combining the GWAS and BCAC data within a meta-analysis, we estimated main effects on breast cancer risk as well as risks for estrogen receptor (ER) and age defined subgroups. Five miRNA binding site SNPs associated significantly with breast cancer risk: rs1045494 (odds ratio (OR) 0.92; 95% confidence interval (CI): 0.88-0.96), rs1052532 (OR 0.97; 95% CI: 0.95-0.99), rs10719 (OR 0.97; 95% CI: 0.94-0.99), rs4687554 (OR 0.97; 95% CI: 0.95-0.99, and rs3134615 (OR 1.03; 95% CI: 1.01-1.05) located in the 3' UTR of CASP8, HDDC3, DROSHA, MUSTN1, and MYCL1, respectively. DROSHA belongs to miRNA machinery genes and has a central role in initial miRNA processing. The remaining genes are involved in different molecular functions, including apoptosis and gene expression regulation. Further studies are warranted to elucidate whether the miRNA binding site SNPs are the causative variants for the observed risk effects.

Published
2014
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8. Long non-coding RNAs differentially expressed between normal versus primary breast tumor tissues disclose converse changes to breast cancer-related protein-coding genes.

Author

Kristin Reiche, Katharina Kasack, Stephan Schreiber, Torben Lüders, Eldri U Due, Bjørn Naume, Margit Riis, Vessela N Kristensen, Friedemann Horn, Anne-Lise Børresen-Dale, Jörg Hackermüller, and Lars O Baumbusch

Subjects
Medicine, Science
Abstract

Breast cancer, the second leading cause of cancer death in women, is a highly heterogeneous disease, characterized by distinct genomic and transcriptomic profiles. Transcriptome analyses prevalently assessed protein-coding genes; however, the majority of the mammalian genome is expressed in numerous non-coding transcripts. Emerging evidence supports that many of these non-coding RNAs are specifically expressed during development, tumorigenesis, and metastasis. The focus of this study was to investigate the expression features and molecular characteristics of long non-coding RNAs (lncRNAs) in breast cancer. We investigated 26 breast tumor and 5 normal tissue samples utilizing a custom expression microarray enclosing probes for mRNAs as well as novel and previously identified lncRNAs. We identified more than 19,000 unique regions significantly differentially expressed between normal versus breast tumor tissue, half of these regions were non-coding without any evidence for functional open reading frames or sequence similarity to known proteins. The identified non-coding regions were primarily located in introns (53%) or in the intergenic space (33%), frequently orientated in antisense-direction of protein-coding genes (14%), and commonly distributed at promoter-, transcription factor binding-, or enhancer-sites. Analyzing the most diverse mRNA breast cancer subtypes Basal-like versus Luminal A and B resulted in 3,025 significantly differentially expressed unique loci, including 682 (23%) for non-coding transcripts. A notable number of differentially expressed protein-coding genes displayed non-synonymous expression changes compared to their nearest differentially expressed lncRNA, including an antisense lncRNA strongly anticorrelated to the mRNA coding for histone deacetylase 3 (HDAC3), which was investigated in more detail. Previously identified chromatin-associated lncRNAs (CARs) were predominantly downregulated in breast tumor samples, including CARs located in the protein-coding genes for CALD1, FTX, and HNRNPH1. In conclusion, a number of differentially expressed lncRNAs have been identified with relation to cancer-related protein-coding genes.

Published
2014
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9. Improving breast cancer survival analysis through competition-based multidimensional modeling.

Author

Erhan Bilal, Janusz Dutkowski, Justin Guinney, In Sock Jang, Benjamin A Logsdon, Gaurav Pandey, Benjamin A Sauerwine, Yishai Shimoni, Hans Kristian Moen Vollan, Brigham H Mecham, Oscar M Rueda, Jorg Tost, Christina Curtis, Mariano J Alvarez, Vessela N Kristensen, Samuel Aparicio, Anne-Lise Børresen-Dale, Carlos Caldas, Andrea Califano, Stephen H Friend, Trey Ideker, Eric E Schadt, Gustavo A Stolovitzky, and Adam A Margolin

Subjects
Biology (General), QH301-705.5
Abstract

Breast cancer is the most common malignancy in women and is responsible for hundreds of thousands of deaths annually. As with most cancers, it is a heterogeneous disease and different breast cancer subtypes are treated differently. Understanding the difference in prognosis for breast cancer based on its molecular and phenotypic features is one avenue for improving treatment by matching the proper treatment with molecular subtypes of the disease. In this work, we employed a competition-based approach to modeling breast cancer prognosis using large datasets containing genomic and clinical information and an online real-time leaderboard program used to speed feedback to the modeling team and to encourage each modeler to work towards achieving a higher ranked submission. We find that machine learning methods combined with molecular features selected based on expert prior knowledge can improve survival predictions compared to current best-in-class methodologies and that ensemble models trained across multiple user submissions systematically outperform individual models within the ensemble. We also find that model scores are highly consistent across multiple independent evaluations. This study serves as the pilot phase of a much larger competition open to the whole research community, with the goal of understanding general strategies for model optimization using clinical and molecular profiling data and providing an objective, transparent system for assessing prognostic models.

Published
2013
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10. Identifying in-trans process associated genes in breast cancer by integrated analysis of copy number and expression data.

Author

Miriam Ragle Aure, Israel Steinfeld, Lars Oliver Baumbusch, Knut Liestøl, Doron Lipson, Sandra Nyberg, Bjørn Naume, Kristine Kleivi Sahlberg, Vessela N Kristensen, Anne-Lise Børresen-Dale, Ole Christian Lingjærde, and Zohar Yakhini

Subjects
Medicine, Science
Abstract

Genomic copy number alterations are common in cancer. Finding the genes causally implicated in oncogenesis is challenging because the gain or loss of a chromosomal region may affect a few key driver genes and many passengers. Integrative analyses have opened new vistas for addressing this issue. One approach is to identify genes with frequent copy number alterations and corresponding changes in expression. Several methods also analyse effects of transcriptional changes on known pathways. Here, we propose a method that analyses in-cis correlated genes for evidence of in-trans association to biological processes, with no bias towards processes of a particular type or function. The method aims to identify cis-regulated genes for which the expression correlation to other genes provides further evidence of a network-perturbing role in cancer. The proposed unsupervised approach involves a sequence of statistical tests to systematically narrow down the list of relevant genes, based on integrative analysis of copy number and gene expression data. A novel adjustment method handles confounding effects of co-occurring copy number aberrations, potentially a large source of false positives in such studies. Applying the method to whole-genome copy number and expression data from 100 primary breast carcinomas, 6373 genes were identified as commonly aberrant, 578 were highly in-cis correlated, and 56 were in addition associated in-trans to biological processes. Among these in-trans process associated and cis-correlated (iPAC) genes, 28% have previously been reported as breast cancer associated, and 64% as cancer associated. By combining statistical evidence from three separate subanalyses that focus respectively on copy number, gene expression and the combination of the two, the proposed method identifies several known and novel cancer driver candidates. Validation in an independent data set supports the conclusion that the method identifies genes implicated in cancer.

Published
2013
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11. miRNA-mRNA integrated analysis reveals roles for miRNAs in primary breast tumors.

Author

Espen Enerly, Israel Steinfeld, Kristine Kleivi, Suvi-Katri Leivonen, Miriam R Aure, Hege G Russnes, Jo Anders Rønneberg, Hilde Johnsen, Roy Navon, Einar Rødland, Rami Mäkelä, Bjørn Naume, Merja Perälä, Olli Kallioniemi, Vessela N Kristensen, Zohar Yakhini, and Anne-Lise Børresen-Dale

Subjects
Medicine, Science
Abstract

IntroductionFew studies have performed expression profiling of both miRNA and mRNA from the same primary breast carcinomas. In this study we present and analyze data derived from expression profiling of 799 miRNAs in 101 primary human breast tumors, along with genome-wide mRNA profiles and extensive clinical information.MethodsWe investigate the relationship between these molecular components, in terms of their correlation with each other and with clinical characteristics. We use a systems biology approach to examine the correlative relationship between miRNA and mRNAs using statistical enrichment methods.ResultsWe identify statistical significant differential expression of miRNAs between molecular intrinsic subtypes, and between samples with different levels of proliferation. Specifically, we point to miRNAs significantly associated with TP53 and ER status. We also show that several cellular processes, such as proliferation, cell adhesion and immune response, are strongly associated with certain miRNAs. We validate the role of miRNAs in regulating proliferation using high-throughput lysate-microarrays on cell lines and point to potential drivers of this process.ConclusionThis study provides a comprehensive dataset as well as methods and system-level results that jointly form a basis for further work on understanding the role of miRNA in primary breast cancer.

Published
2011
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12. Combining gene signatures improves prediction of breast cancer survival.

Author

Xi Zhao, Einar Andreas Rødland, Therese Sørlie, Bjørn Naume, Anita Langerød, Arnoldo Frigessi, Vessela N Kristensen, Anne-Lise Børresen-Dale, and Ole Christian Lingjærde

Subjects
Medicine, Science
Abstract

BACKGROUND: Several gene sets for prediction of breast cancer survival have been derived from whole-genome mRNA expression profiles. Here, we develop a statistical framework to explore whether combination of the information from such sets may improve prediction of recurrence and breast cancer specific death in early-stage breast cancers. Microarray data from two clinically similar cohorts of breast cancer patients are used as training (n = 123) and test set (n = 81), respectively. Gene sets from eleven previously published gene signatures are included in the study. PRINCIPAL FINDINGS: To investigate the relationship between breast cancer survival and gene expression on a particular gene set, a Cox proportional hazards model is applied using partial likelihood regression with an L2 penalty to avoid overfitting and using cross-validation to determine the penalty weight. The fitted models are applied to an independent test set to obtain a predicted risk for each individual and each gene set. Hierarchical clustering of the test individuals on the basis of the vector of predicted risks results in two clusters with distinct clinical characteristics in terms of the distribution of molecular subtypes, ER, PR status, TP53 mutation status and histological grade category, and associated with significantly different survival probabilities (recurrence: p = 0.005; breast cancer death: p = 0.014). Finally, principal components analysis of the gene signatures is used to derive combined predictors used to fit a new Cox model. This model classifies test individuals into two risk groups with distinct survival characteristics (recurrence: p = 0.003; breast cancer death: p = 0.001). The latter classifier outperforms all the individual gene signatures, as well as Cox models based on traditional clinical parameters and the Adjuvant! Online for survival prediction. CONCLUSION: Combining the predictive strength of multiple gene signatures improves prediction of breast cancer survival. The presented methodology is broadly applicable to breast cancer risk assessment using any new identified gene set.

Published
2011
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15. Supplementary Data from A Mitochondrial Target Sequence Polymorphism in Manganese Superoxide Dismutase Predicts Inferior Survival in Breast Cancer Patients Treated with Cyclophosphamide

Author

Stefan Ambs, David A. Wink, Stephen J. Chanock, Vessela N. Kristensen, Bjorn Naume, Anne-Lise Børresen-Dale, Per E. Lønning, Lisa A. Ridnour, Julie E. Goodman, Stephanie B. Geisler, Hege Edvardsen, Tiffany M. Howe, Brenda J. Boersma, and Sharon A. Glynn

Abstract

Supplementary Data from A Mitochondrial Target Sequence Polymorphism in Manganese Superoxide Dismutase Predicts Inferior Survival in Breast Cancer Patients Treated with Cyclophosphamide

Published
2023
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16. Supplementary Figure legends from DNA Methylation Status of Key Cell-Cycle Regulators Such as CDKNA2/p16 and CCNA1 Correlates with Treatment Response to Doxorubicin and 5-Fluorouracil in Locally Advanced Breast Tumors

Author

Vessela N. Kristensen, Jörg Tost, Per Eystein Lønning, Anne-Lise Børresen-Dale, Ida Bukholm, Thomas Fleischer, Nizar Touleimat, Hege Edvardsen, Florence Busato, and Jovana Klajic

Abstract

Supplementary Figure legends. supplementary data

Published
2023
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17. Data from DNA Methylation Status of Key Cell-Cycle Regulators Such as CDKNA2/p16 and CCNA1 Correlates with Treatment Response to Doxorubicin and 5-Fluorouracil in Locally Advanced Breast Tumors

Author

Vessela N. Kristensen, Jörg Tost, Per Eystein Lønning, Anne-Lise Børresen-Dale, Ida Bukholm, Thomas Fleischer, Nizar Touleimat, Hege Edvardsen, Florence Busato, and Jovana Klajic

Abstract

Purpose: To explore alterations in gene promoter methylation as a potential cause of acquired drug resistance to doxorubicin or combined treatment with 5-fluorouracil and mitomycin C in human breast cancers.Experimental Design: Paired tumor samples from locally advanced breast cancer patients treated with doxorubicin and 5-fluorouracil-mitomycin C were used in the genome-wide DNA methylation analysis as discovery cohort. An enlarged cohort from the same two prospective studies as those in the discovery cohort was used as a validation set in pyrosequencing analysis.Results: A total of 469 genes were differentially methylated after treatment with doxorubicin and revealed a significant association with canonical pathways enriched for immune cell response and cell-cycle regulating genes including CDKN2A, CCND2, CCNA1, which were also associated to treatment response. Treatment with FUMI resulted in 343 differentially methylated genes representing canonical pathways such as retinoate biosynthesis, gαi signaling, and LXR/RXR activation. Despite the clearly different genes and pathways involved in the metabolism and therapeutic effect of both drugs, 46 genes were differentially methylated before and after treatment with both doxorubicin and FUMI. DNA methylation profiles in genes such as BRCA1, FOXC1, and IGFBP3, and most notably repetitive elements like ALU and LINE1, were associated with TP53 mutations status.Conclusion: We identified and validated key cell-cycle regulators differentially methylated before and after neoadjuvant chemotherapy such as CDKN2A and CCNA1 and reported that methylation patterns of these genes may be potential predictive markers to anthracycline/mitomycine sensitivity. Clin Cancer Res; 20(24); 6357–66. ©2014 AACR.

Published
2023
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18. Supplementary Figure 6 from TP53 Mutation Spectrum in Breast Cancer Is Subtype Specific and Has Distinct Prognostic Relevance

Author

Anita Langerød, Carlos Caldas, Anne-Lise Børresen-Dale, Samuel Aparicio, Vessela N. Kristensen, David A. Quigley, Tomo Osako, Steven McKinney, Oscar M. Rueda, Suet-Feung Chin, Hans Kristian Moen Vollan, and Laxmi Silwal-Pandit

Abstract

PDF file - 29KB, Figure S6. Kaplan-Meier survival curves. A. OS of TP53 mutation status in whole cohort B. BCSS of TP53 mutation status in patients not treated with adjuvant chemotherapy. Numbers at risk are listed below each chart e = no. of all deaths (OS) or Breast Cancer specific deaths (BCSS).

Published
2023
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19. Data from Genetic Risk Score Mendelian Randomization Shows that Obesity Measured as Body Mass Index, but not Waist:Hip Ratio, Is Causal for Endometrial Cancer

Author

Amanda B. Spurdle, Deborah J. Thompson, Douglas F. Easton, Alison M. Dunning, Ian Tomlinson, Paul D.P. Pharoah, Angela Cox, Vessela N. Kristensen, Graham G. Giles, Fergus J. Couch, Jenny Chang-Claude, Annika Lindblom, Hiltrud Brauch, Alfons Meindl, Hermann Brenner, Barbara Burwinkel, Anthony J. Swerdlow, Julian Peto, John L. Hopper, Jonathan P. Tyrer, Qin Wang, Kyriaki Michailidou, Manjeet K. Bolla, Emma Tham, Miriam Mints, Anthony Proietto, Geoffrey Otton, Katie A. Ashton, Henrica M.J. Werner, Jone Trovik, Helga B. Salvesen, Tormund S. Njølstad, Stacey J. Winham, Brooke L. Fridley, Ellen L. Goode, Sean C. Dowdy, Julie M. Cunningham, Patrick Neven, Diether Lambrechts, Jeroen Depreeuw, Daniela Annibali, Frederic Amant, Ingo B. Runnebaum, Peter Hillemanns, Matthias Dürst, Thilo Dörk, Jingmei Li, Per Hall, Hatef Darabi, Kamila Czene, Matthias Rübner, Alexander Hein, Peter A. Fasching, Arif B. Ekici, Matthias W. Beckmann, Shirley V. Hodgson, Lynn Martin, Maggie Gorman, Mitul Shah, Catherine S. Healey, Shahana Ahmed, Rodney J. Scott, Mark McEvoy, Elizabeth G. Holliday, Joe Dennis, Timothy Cheng, Sarah E. Medland, John Attia, Penelope M. Webb, Louise Marquart, Tracy A. O'Mara, and Jodie N. Painter

Abstract

Background: The strongest known risk factor for endometrial cancer is obesity. To determine whether SNPs associated with increased body mass index (BMI) or waist–hip ratio (WHR) are associated with endometrial cancer risk, independent of measured BMI, we investigated relationships between 77 BMI and 47 WHR SNPs and endometrial cancer in 6,609 cases and 37,926 country-matched controls.Methods: Logistic regression analysis and fixed effects meta-analysis were used to test for associations between endometrial cancer risk and (i) individual BMI or WHR SNPs, (ii) a combined weighted genetic risk score (wGRS) for BMI or WHR. Causality of BMI for endometrial cancer was assessed using Mendelian randomization, with BMIwGRS as instrumental variable.Results: The BMIwGRS was significantly associated with endometrial cancer risk (P = 3.4 × 10−17). Scaling the effect of the BMIwGRS on endometrial cancer risk by its effect on BMI, the endometrial cancer OR per 5 kg/m2 of genetically predicted BMI was 2.06 [95% confidence interval (CI), 1.89–2.21], larger than the observed effect of BMI on endometrial cancer risk (OR = 1.55; 95% CI, 1.44–1.68, per 5 kg/m2). The association attenuated but remained significant after adjusting for BMI (OR = 1.22; 95% CI, 1.10–1.39; P = 5.3 × 10−4). There was evidence of directional pleiotropy (P = 1.5 × 10−4). BMI SNP rs2075650 was associated with endometrial cancer at study-wide significance (P < 4.0 × 10−4), independent of BMI. Endometrial cancer was not significantly associated with individual WHR SNPs or the WHRwGRS.Conclusions: BMI, but not WHR, is causally associated with endometrial cancer risk, with evidence that some BMI-associated SNPs alter endometrial cancer risk via mechanisms other than measurable BMI.Impact: The causal association between BMI SNPs and endometrial cancer has possible implications for endometrial cancer risk modeling. Cancer Epidemiol Biomarkers Prev; 25(11); 1503–10. ©2016 AACR.

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2023
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20. Supplementary Tables 1 - 4 from Common Breast Cancer Susceptibility Variants in LSP1 and RAD51L1 Are Associated with Mammographic Density Measures that Predict Breast Cancer Risk

Author

Isabel dos-Santos-Silva, John L. Hopper, V. Shane Pankratz, Anna H. Wu, Tina Audley, Kamila Czene, Louise Eriksson, JianJun Liu, Edyta Reszka, Agnieszka Bukowska, Beata Peplonska, Ewa Wesolowska, Jolanta Lissowska, Jonine D. Figuero, Norman F. Boyd, Julia A. Knight, Irene L. Andrulis, David J. Hunter, Susan E. Hankinson, Sara Lindström, Anne-Lise Børresen-Dale, Vessela N. Kristensen, Inger Torhild Gram, Grethe I.G. Alnaes, Margarethe Biong, Valeria A. McCormack, Nichola Johnson, Kate Walker, Christopher A. Haiman, Gertraud Maskarinec, Christy Woolcott, Laurence N. Kolonel, Loic Le Marchand, Melissa C. Southey, Kavitha Krishnan, Laura Baglietto, Graham G. Giles, Fergus J. Couch, Gek Kwan-Lim, Martin O. Leach, Rosalind A. Eeles, Susan J. Ramus, Simon A. Gayther, Eunjung Lee, Giske Ursin, Kay-Tee Khaw, Ruth J.F. Loos, Nicholas J. Wareham, Ruth M.L. Warren, Robert N. Luben, Deborah J. Thompson, Douglas F. Easton, Jajini S. Varghese, Jean Leyland, Judith Brown, Petra H.M. Peeters, N. Charlotte Onland-Moret, Mariëtte Lokate, Carla H. van Gils, Javier Benitez, Anna Gonzalez-Neira, Pablo Fernández-Navarro, Marina Pollan, Matthias W. Beckmann, Katharina Heusinger, Sebastian M. Jud, Gretchen L. Gierach, Fabrice Odefrey, Carmel Apicella, Jennifer Stone, Jingmei Li, Rulla M. Tamimi, Per Hall, Peter A. Fasching, Christopher G. Scott, and Celine M. Vachon

Abstract

PDF file, 120KB, Supplemental Table 1. Study Descriptions, Design and Sample Size for 19 DENSNP Studies. Caucasian Women Only. Supplemental Table 2. Source and Timing of Reproductive and Anthropometric Variable Collection. Supplemental Table 3. Description of Mammogram View, Side, Film Digitizer and Density Estimation Software for DENSNP Studies. Supplemental Table 4. Genotyping Platform and SNP Call Rates Performed by Each Study.

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2023
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21. Supplementary Figure 8 from TP53 Mutation Spectrum in Breast Cancer Is Subtype Specific and Has Distinct Prognostic Relevance

Author

Anita Langerød, Carlos Caldas, Anne-Lise Børresen-Dale, Samuel Aparicio, Vessela N. Kristensen, David A. Quigley, Tomo Osako, Steven McKinney, Oscar M. Rueda, Suet-Feung Chin, Hans Kristian Moen Vollan, and Laxmi Silwal-Pandit

Abstract

PDF file - 40KB, Figure S8. A. Frequency of TP53 mutations and LOH in PAM50 and IC subtypes. B. Kaplan-Meier survival curves showing BCSS of TP53 mutation status in combination with LOH and MDM2 amplification. C. BCSS of TP53 mutation status combined with lymphocyte infiltration in ER negative patients. Numbers at risk are listed below each chart e = no. of all deaths (OS) or Breast Cancer specific deaths (BCSS).

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2023
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22. Supplementary Figure 1 from DNA Methylation Status of Key Cell-Cycle Regulators Such as CDKNA2/p16 and CCNA1 Correlates with Treatment Response to Doxorubicin and 5-Fluorouracil in Locally Advanced Breast Tumors

Author

Vessela N. Kristensen, Jörg Tost, Per Eystein Lønning, Anne-Lise Børresen-Dale, Ida Bukholm, Thomas Fleischer, Nizar Touleimat, Hege Edvardsen, Florence Busato, and Jovana Klajic

Abstract

Supplementary Figure 1. Canonical pathway:Communication between Innate and Adaptive Immune Cells

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2023
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23. Supplemental Table 2. Average BMIs in the ANECS, SEARCH and iCOGS endometrial cancer datasets. from Genetic Risk Score Mendelian Randomization Shows that Obesity Measured as Body Mass Index, but not Waist:Hip Ratio, Is Causal for Endometrial Cancer

Author

Amanda B. Spurdle, Deborah J. Thompson, Douglas F. Easton, Alison M. Dunning, Ian Tomlinson, Paul D.P. Pharoah, Angela Cox, Vessela N. Kristensen, Graham G. Giles, Fergus J. Couch, Jenny Chang-Claude, Annika Lindblom, Hiltrud Brauch, Alfons Meindl, Hermann Brenner, Barbara Burwinkel, Anthony J. Swerdlow, Julian Peto, John L. Hopper, Jonathan P. Tyrer, Qin Wang, Kyriaki Michailidou, Manjeet K. Bolla, Emma Tham, Miriam Mints, Anthony Proietto, Geoffrey Otton, Katie A. Ashton, Henrica M.J. Werner, Jone Trovik, Helga B. Salvesen, Tormund S. Njølstad, Stacey J. Winham, Brooke L. Fridley, Ellen L. Goode, Sean C. Dowdy, Julie M. Cunningham, Patrick Neven, Diether Lambrechts, Jeroen Depreeuw, Daniela Annibali, Frederic Amant, Ingo B. Runnebaum, Peter Hillemanns, Matthias Dürst, Thilo Dörk, Jingmei Li, Per Hall, Hatef Darabi, Kamila Czene, Matthias Rübner, Alexander Hein, Peter A. Fasching, Arif B. Ekici, Matthias W. Beckmann, Shirley V. Hodgson, Lynn Martin, Maggie Gorman, Mitul Shah, Catherine S. Healey, Shahana Ahmed, Rodney J. Scott, Mark McEvoy, Elizabeth G. Holliday, Joe Dennis, Timothy Cheng, Sarah E. Medland, John Attia, Penelope M. Webb, Louise Marquart, Tracy A. O'Mara, and Jodie N. Painter

Abstract

Excel file detailing average BMI per study population

Published
2023
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24. Supplementary Table 5 from Common Breast Cancer Susceptibility Variants in LSP1 and RAD51L1 Are Associated with Mammographic Density Measures that Predict Breast Cancer Risk

Author

Isabel dos-Santos-Silva, John L. Hopper, V. Shane Pankratz, Anna H. Wu, Tina Audley, Kamila Czene, Louise Eriksson, JianJun Liu, Edyta Reszka, Agnieszka Bukowska, Beata Peplonska, Ewa Wesolowska, Jolanta Lissowska, Jonine D. Figuero, Norman F. Boyd, Julia A. Knight, Irene L. Andrulis, David J. Hunter, Susan E. Hankinson, Sara Lindström, Anne-Lise Børresen-Dale, Vessela N. Kristensen, Inger Torhild Gram, Grethe I.G. Alnaes, Margarethe Biong, Valeria A. McCormack, Nichola Johnson, Kate Walker, Christopher A. Haiman, Gertraud Maskarinec, Christy Woolcott, Laurence N. Kolonel, Loic Le Marchand, Melissa C. Southey, Kavitha Krishnan, Laura Baglietto, Graham G. Giles, Fergus J. Couch, Gek Kwan-Lim, Martin O. Leach, Rosalind A. Eeles, Susan J. Ramus, Simon A. Gayther, Eunjung Lee, Giske Ursin, Kay-Tee Khaw, Ruth J.F. Loos, Nicholas J. Wareham, Ruth M.L. Warren, Robert N. Luben, Deborah J. Thompson, Douglas F. Easton, Jajini S. Varghese, Jean Leyland, Judith Brown, Petra H.M. Peeters, N. Charlotte Onland-Moret, Mariëtte Lokate, Carla H. van Gils, Javier Benitez, Anna Gonzalez-Neira, Pablo Fernández-Navarro, Marina Pollan, Matthias W. Beckmann, Katharina Heusinger, Sebastian M. Jud, Gretchen L. Gierach, Fabrice Odefrey, Carmel Apicella, Jennifer Stone, Jingmei Li, Rulla M. Tamimi, Per Hall, Peter A. Fasching, Christopher G. Scott, and Celine M. Vachon

Abstract

PDF file, 20KB, Supplemental Table 5a. Associations of Common Breast Cancer Susceptibility Variants with Adjusted Percent Mammographic Density. Supplemental Table 5b. Associations of Common Breast Cancer Susceptibility Variants with Adjusted Mammographic Dense Area. Supplemental Table 5c. Associations of Common Breast Cancer Susceptibility Variants with Adjusted Mammographic Non Dense Area.

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2023
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25. Supplementary Figure 5 from TP53 Mutation Spectrum in Breast Cancer Is Subtype Specific and Has Distinct Prognostic Relevance

Author

Anita Langerød, Carlos Caldas, Anne-Lise Børresen-Dale, Samuel Aparicio, Vessela N. Kristensen, David A. Quigley, Tomo Osako, Steven McKinney, Oscar M. Rueda, Suet-Feung Chin, Hans Kristian Moen Vollan, and Laxmi Silwal-Pandit

Abstract

PDF file - 162KB, Figure S5. Boxplot diagram showing APOBEC3B mRNA expression levels as log intensity values in TP53 mutated and wildtype samples stratified by PAM50 subtypes. The bold black line represents the mean APOBEC3B expression value and the box represents 25th and 75th percentiles.

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2023
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26. Supplemental Table 1. Details of cases and controls included in the endometrial cancer analyses from Genetic Risk Score Mendelian Randomization Shows that Obesity Measured as Body Mass Index, but not Waist:Hip Ratio, Is Causal for Endometrial Cancer

Author

Amanda B. Spurdle, Deborah J. Thompson, Douglas F. Easton, Alison M. Dunning, Ian Tomlinson, Paul D.P. Pharoah, Angela Cox, Vessela N. Kristensen, Graham G. Giles, Fergus J. Couch, Jenny Chang-Claude, Annika Lindblom, Hiltrud Brauch, Alfons Meindl, Hermann Brenner, Barbara Burwinkel, Anthony J. Swerdlow, Julian Peto, John L. Hopper, Jonathan P. Tyrer, Qin Wang, Kyriaki Michailidou, Manjeet K. Bolla, Emma Tham, Miriam Mints, Anthony Proietto, Geoffrey Otton, Katie A. Ashton, Henrica M.J. Werner, Jone Trovik, Helga B. Salvesen, Tormund S. Njølstad, Stacey J. Winham, Brooke L. Fridley, Ellen L. Goode, Sean C. Dowdy, Julie M. Cunningham, Patrick Neven, Diether Lambrechts, Jeroen Depreeuw, Daniela Annibali, Frederic Amant, Ingo B. Runnebaum, Peter Hillemanns, Matthias Dürst, Thilo Dörk, Jingmei Li, Per Hall, Hatef Darabi, Kamila Czene, Matthias Rübner, Alexander Hein, Peter A. Fasching, Arif B. Ekici, Matthias W. Beckmann, Shirley V. Hodgson, Lynn Martin, Maggie Gorman, Mitul Shah, Catherine S. Healey, Shahana Ahmed, Rodney J. Scott, Mark McEvoy, Elizabeth G. Holliday, Joe Dennis, Timothy Cheng, Sarah E. Medland, John Attia, Penelope M. Webb, Louise Marquart, Tracy A. O'Mara, and Jodie N. Painter

Abstract

Excel file detailing case-control populations

Published
2023
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27. Data from Toward Personalized Computer Simulation of Breast Cancer Treatment: A Multiscale Pharmacokinetic and Pharmacodynamic Model Informed by Multitype Patient Data

Author

Arnoldo Frigessi, Alvaro Köhn-Luque, Olav Engebraaten, Vessela N. Kristensen, Anne-Lise Børresen-Dale, Therese Seierstad, Marie E. Rognes, Surendra Kumar, Simon W. Funke, Elin Borgen, Øystein Garred, Thomas Fleischer, Oliver M. Geier, and Xiaoran Lai

Abstract

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.Significance:Mathematical modeling is used to validate possible mechanisms of tumor growth, resistance, and treatment outcome.

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2023
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28. Supplementary Figure 4 from TP53 Mutation Spectrum in Breast Cancer Is Subtype Specific and Has Distinct Prognostic Relevance

Author

Anita Langerød, Carlos Caldas, Anne-Lise Børresen-Dale, Samuel Aparicio, Vessela N. Kristensen, David A. Quigley, Tomo Osako, Steven McKinney, Oscar M. Rueda, Suet-Feung Chin, Hans Kristian Moen Vollan, and Laxmi Silwal-Pandit

Abstract

PDF file - 22KB, Figure S4. Distribution of TP53 mutations in IC subtypes. A. METABRIC cohort. B. TCGA cohort.

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2023
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29. Supplementary Figure 2 from TP53 Mutation Spectrum in Breast Cancer Is Subtype Specific and Has Distinct Prognostic Relevance

Author

Anita Langerød, Carlos Caldas, Anne-Lise Børresen-Dale, Samuel Aparicio, Vessela N. Kristensen, David A. Quigley, Tomo Osako, Steven McKinney, Oscar M. Rueda, Suet-Feung Chin, Hans Kristian Moen Vollan, and Laxmi Silwal-Pandit

Abstract

PDF file - 26KB, Figure S2. Distribution of TP53 mutation class in breast cancer subtypes. A. PAM50 and B. IC subtypes.

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2023
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30. Supplementary Table 3: Association of 77 body mass index (BMI) SNPs with endometrial cancer risk and BMI in the endometrial cancer dataset from Genetic Risk Score Mendelian Randomization Shows that Obesity Measured as Body Mass Index, but not Waist:Hip Ratio, Is Causal for Endometrial Cancer

Author

Amanda B. Spurdle, Deborah J. Thompson, Douglas F. Easton, Alison M. Dunning, Ian Tomlinson, Paul D.P. Pharoah, Angela Cox, Vessela N. Kristensen, Graham G. Giles, Fergus J. Couch, Jenny Chang-Claude, Annika Lindblom, Hiltrud Brauch, Alfons Meindl, Hermann Brenner, Barbara Burwinkel, Anthony J. Swerdlow, Julian Peto, John L. Hopper, Jonathan P. Tyrer, Qin Wang, Kyriaki Michailidou, Manjeet K. Bolla, Emma Tham, Miriam Mints, Anthony Proietto, Geoffrey Otton, Katie A. Ashton, Henrica M.J. Werner, Jone Trovik, Helga B. Salvesen, Tormund S. Njølstad, Stacey J. Winham, Brooke L. Fridley, Ellen L. Goode, Sean C. Dowdy, Julie M. Cunningham, Patrick Neven, Diether Lambrechts, Jeroen Depreeuw, Daniela Annibali, Frederic Amant, Ingo B. Runnebaum, Peter Hillemanns, Matthias Dürst, Thilo Dörk, Jingmei Li, Per Hall, Hatef Darabi, Kamila Czene, Matthias Rübner, Alexander Hein, Peter A. Fasching, Arif B. Ekici, Matthias W. Beckmann, Shirley V. Hodgson, Lynn Martin, Maggie Gorman, Mitul Shah, Catherine S. Healey, Shahana Ahmed, Rodney J. Scott, Mark McEvoy, Elizabeth G. Holliday, Joe Dennis, Timothy Cheng, Sarah E. Medland, John Attia, Penelope M. Webb, Louise Marquart, Tracy A. O'Mara, and Jodie N. Painter

Abstract

Excel file detailing single BMI SNP associations with EC risk and BMI

Published
2023
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31. Supplementary Table 6 from Common Breast Cancer Susceptibility Variants in LSP1 and RAD51L1 Are Associated with Mammographic Density Measures that Predict Breast Cancer Risk

Author

Isabel dos-Santos-Silva, John L. Hopper, V. Shane Pankratz, Anna H. Wu, Tina Audley, Kamila Czene, Louise Eriksson, JianJun Liu, Edyta Reszka, Agnieszka Bukowska, Beata Peplonska, Ewa Wesolowska, Jolanta Lissowska, Jonine D. Figuero, Norman F. Boyd, Julia A. Knight, Irene L. Andrulis, David J. Hunter, Susan E. Hankinson, Sara Lindström, Anne-Lise Børresen-Dale, Vessela N. Kristensen, Inger Torhild Gram, Grethe I.G. Alnaes, Margarethe Biong, Valeria A. McCormack, Nichola Johnson, Kate Walker, Christopher A. Haiman, Gertraud Maskarinec, Christy Woolcott, Laurence N. Kolonel, Loic Le Marchand, Melissa C. Southey, Kavitha Krishnan, Laura Baglietto, Graham G. Giles, Fergus J. Couch, Gek Kwan-Lim, Martin O. Leach, Rosalind A. Eeles, Susan J. Ramus, Simon A. Gayther, Eunjung Lee, Giske Ursin, Kay-Tee Khaw, Ruth J.F. Loos, Nicholas J. Wareham, Ruth M.L. Warren, Robert N. Luben, Deborah J. Thompson, Douglas F. Easton, Jajini S. Varghese, Jean Leyland, Judith Brown, Petra H.M. Peeters, N. Charlotte Onland-Moret, Mariëtte Lokate, Carla H. van Gils, Javier Benitez, Anna Gonzalez-Neira, Pablo Fernández-Navarro, Marina Pollan, Matthias W. Beckmann, Katharina Heusinger, Sebastian M. Jud, Gretchen L. Gierach, Fabrice Odefrey, Carmel Apicella, Jennifer Stone, Jingmei Li, Rulla M. Tamimi, Per Hall, Peter A. Fasching, Christopher G. Scott, and Celine M. Vachon

Abstract

PDF file, 133KB, Supplemental Table 6a. Per-Allele Association (SE) of Breast Cancer Susceptibility Variants with Mammographic Percent Density Stratifying by Case Status, BMI, Menopausal Status and PMH Use. Supplemental Table 6b. Per-Allele Associations (SE) of Breast Cancer Susceptibility Variants with Adjusted Mammographic Dense Area Stratifying by Case Status, BMI Menopausal Status and PMH Use. Supplemental Table 6c. Per-Allele Associations (SE) of Breast Cancer Susceptibility Variants with Adjusted Mammographic Non Dense Area Stratifying by Case Status, BMI Menopausal Status and PMH Use.

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2023
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32. Supplementary Table 4. Association of 47 waist-hip ratio (WHR) SNPs with endometrial cancer risk from Genetic Risk Score Mendelian Randomization Shows that Obesity Measured as Body Mass Index, but not Waist:Hip Ratio, Is Causal for Endometrial Cancer

Author

Amanda B. Spurdle, Deborah J. Thompson, Douglas F. Easton, Alison M. Dunning, Ian Tomlinson, Paul D.P. Pharoah, Angela Cox, Vessela N. Kristensen, Graham G. Giles, Fergus J. Couch, Jenny Chang-Claude, Annika Lindblom, Hiltrud Brauch, Alfons Meindl, Hermann Brenner, Barbara Burwinkel, Anthony J. Swerdlow, Julian Peto, John L. Hopper, Jonathan P. Tyrer, Qin Wang, Kyriaki Michailidou, Manjeet K. Bolla, Emma Tham, Miriam Mints, Anthony Proietto, Geoffrey Otton, Katie A. Ashton, Henrica M.J. Werner, Jone Trovik, Helga B. Salvesen, Tormund S. Njølstad, Stacey J. Winham, Brooke L. Fridley, Ellen L. Goode, Sean C. Dowdy, Julie M. Cunningham, Patrick Neven, Diether Lambrechts, Jeroen Depreeuw, Daniela Annibali, Frederic Amant, Ingo B. Runnebaum, Peter Hillemanns, Matthias Dürst, Thilo Dörk, Jingmei Li, Per Hall, Hatef Darabi, Kamila Czene, Matthias Rübner, Alexander Hein, Peter A. Fasching, Arif B. Ekici, Matthias W. Beckmann, Shirley V. Hodgson, Lynn Martin, Maggie Gorman, Mitul Shah, Catherine S. Healey, Shahana Ahmed, Rodney J. Scott, Mark McEvoy, Elizabeth G. Holliday, Joe Dennis, Timothy Cheng, Sarah E. Medland, John Attia, Penelope M. Webb, Louise Marquart, Tracy A. O'Mara, and Jodie N. Painter

Abstract

Excel file detailing single WHR SNP associations with EC risk

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2023
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33. Data from TP53 Mutation Spectrum in Breast Cancer Is Subtype Specific and Has Distinct Prognostic Relevance

Author

Anita Langerød, Carlos Caldas, Anne-Lise Børresen-Dale, Samuel Aparicio, Vessela N. Kristensen, David A. Quigley, Tomo Osako, Steven McKinney, Oscar M. Rueda, Suet-Feung Chin, Hans Kristian Moen Vollan, and Laxmi Silwal-Pandit

Abstract

Purpose: In breast cancer, the TP53 gene is frequently mutated and the mutations have been associated with poor prognosis. The prognostic impact of the different types of TP53 mutations across the different molecular subtypes is still poorly understood. Here, we characterize the spectrum and prognostic significance of TP53 mutations with respect to the PAM50 subtypes and integrative clusters (IC).Experimental Design:TP53 mutation status was obtained for 1,420 tumor samples from the METABRIC cohort by sequencing all coding exons using the Sanger method.Results:TP53 mutations were found in 28.3% of the tumors, conferring a worse overall and breast cancer-specific survival [HR = 2.03; 95% confidence interval (CI), 1.65–2.48, P < 0.001], and were also found to be an independent marker of poor prognosis in estrogen receptor-positive cases (HR = 1.86; 95% CI, 1.39–2.49, P < 0.001). The mutation spectrum of TP53 varied between the breast cancer subtypes, and individual alterations showed subtype-specific association. TP53 mutations were associated with increased mortality in patients with luminal B, HER2-enriched, and normal-like tumors, but not in patients with luminal A and basal-like tumors. Similar observations were made in ICs, where mutation associated with poorer outcome in IC1, IC4, and IC5. The combined effect of TP53 mutation, TP53 LOH, and MDM2 amplification on mortality was additive.Conclusion: This study reveals that TP53 mutations have different clinical relevance in molecular subtypes of breast cancer, and suggests diverse roles for TP53 in the biology underlying breast cancer development. Clin Cancer Res; 20(13); 3569–80. ©2014 AACR.

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2023
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34. Supplementary Table 1-6 from DNA Methylation Status of Key Cell-Cycle Regulators Such as CDKNA2/p16 and CCNA1 Correlates with Treatment Response to Doxorubicin and 5-Fluorouracil in Locally Advanced Breast Tumors

Author

Vessela N. Kristensen, Jörg Tost, Per Eystein Lønning, Anne-Lise Børresen-Dale, Ida Bukholm, Thomas Fleischer, Nizar Touleimat, Hege Edvardsen, Florence Busato, and Jovana Klajic

Abstract

Supplementary Table 1-6. Supplementary Table 1: Differentially methylated genes and functions of the genes before/after treatment with doxorubicin and FUMI Supplementary Table 2: Ingenuity Pathway analysis Supplementary Table 3: List and function of the 46 differentially methylated genes Supplementary Table 4: List of the 333 differentially methylated genes Supplementary Table 5: Ingenuity Pathway analysis of the 333 differentially methylated genes Supplementary Table 6: Associations between methylation status and TP53 mutation status

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2023
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35. Data from Common Breast Cancer Susceptibility Variants in LSP1 and RAD51L1 Are Associated with Mammographic Density Measures that Predict Breast Cancer Risk

Author

Isabel dos-Santos-Silva, John L. Hopper, V. Shane Pankratz, Anna H. Wu, Tina Audley, Kamila Czene, Louise Eriksson, JianJun Liu, Edyta Reszka, Agnieszka Bukowska, Beata Peplonska, Ewa Wesolowska, Jolanta Lissowska, Jonine D. Figuero, Norman F. Boyd, Julia A. Knight, Irene L. Andrulis, David J. Hunter, Susan E. Hankinson, Sara Lindström, Anne-Lise Børresen-Dale, Vessela N. Kristensen, Inger Torhild Gram, Grethe I.G. Alnaes, Margarethe Biong, Valeria A. McCormack, Nichola Johnson, Kate Walker, Christopher A. Haiman, Gertraud Maskarinec, Christy Woolcott, Laurence N. Kolonel, Loic Le Marchand, Melissa C. Southey, Kavitha Krishnan, Laura Baglietto, Graham G. Giles, Fergus J. Couch, Gek Kwan-Lim, Martin O. Leach, Rosalind A. Eeles, Susan J. Ramus, Simon A. Gayther, Eunjung Lee, Giske Ursin, Kay-Tee Khaw, Ruth J.F. Loos, Nicholas J. Wareham, Ruth M.L. Warren, Robert N. Luben, Deborah J. Thompson, Douglas F. Easton, Jajini S. Varghese, Jean Leyland, Judith Brown, Petra H.M. Peeters, N. Charlotte Onland-Moret, Mariëtte Lokate, Carla H. van Gils, Javier Benitez, Anna Gonzalez-Neira, Pablo Fernández-Navarro, Marina Pollan, Matthias W. Beckmann, Katharina Heusinger, Sebastian M. Jud, Gretchen L. Gierach, Fabrice Odefrey, Carmel Apicella, Jennifer Stone, Jingmei Li, Rulla M. Tamimi, Per Hall, Peter A. Fasching, Christopher G. Scott, and Celine M. Vachon

Abstract

Background: Mammographic density adjusted for age and body mass index (BMI) is a heritable marker of breast cancer susceptibility. Little is known about the biologic mechanisms underlying the association between mammographic density and breast cancer risk. We examined whether common low-penetrance breast cancer susceptibility variants contribute to interindividual differences in mammographic density measures.Methods: We established an international consortium (DENSNP) of 19 studies from 10 countries, comprising 16,895 Caucasian women, to conduct a pooled cross-sectional analysis of common breast cancer susceptibility variants in 14 independent loci and mammographic density measures. Dense and nondense areas, and percent density, were measured using interactive-thresholding techniques. Mixed linear models were used to assess the association between genetic variants and the square roots of mammographic density measures adjusted for study, age, case status, BMI, and menopausal status.Results: Consistent with their breast cancer associations, the C-allele of rs3817198 in LSP1 was positively associated with both adjusted dense area (P = 0.00005) and adjusted percent density (P = 0.001), whereas the A-allele of rs10483813 in RAD51L1 was inversely associated with adjusted percent density (P = 0.003), but not with adjusted dense area (P = 0.07).Conclusion: We identified two common breast cancer susceptibility variants associated with mammographic measures of radiodense tissue in the breast gland.Impact: We examined the association of 14 established breast cancer susceptibility loci with mammographic density phenotypes within a large genetic consortium and identified two breast cancer susceptibility variants, LSP1-rs3817198 and RAD51L1-rs10483813, associated with mammographic measures and in the same direction as the breast cancer association. Cancer Epidemiol Biomarkers Prev; 21(7); 1156–. ©2012 AACR.

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2023
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36. Supplementary Figure 2 from DNA Methylation Status of Key Cell-Cycle Regulators Such as CDKNA2/p16 and CCNA1 Correlates with Treatment Response to Doxorubicin and 5-Fluorouracil in Locally Advanced Breast Tumors

Author

Vessela N. Kristensen, Jörg Tost, Per Eystein Lønning, Anne-Lise Børresen-Dale, Ida Bukholm, Thomas Fleischer, Nizar Touleimat, Hege Edvardsen, Florence Busato, and Jovana Klajic

Abstract

Supplementary Figure 2. Canonical pathway:Gαi Signalling

Published
2023
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37. Supplementary Text from Toward Personalized Computer Simulation of Breast Cancer Treatment: A Multiscale Pharmacokinetic and Pharmacodynamic Model Informed by Multitype Patient Data

Author

Arnoldo Frigessi, Alvaro Köhn-Luque, Olav Engebraaten, Vessela N. Kristensen, Anne-Lise Børresen-Dale, Therese Seierstad, Marie E. Rognes, Surendra Kumar, Simon W. Funke, Elin Borgen, Øystein Garred, Thomas Fleischer, Oliver M. Geier, and Xiaoran Lai

Abstract

Supplementary text to the main article detailing mathematical model, model initialisation and parametrisation, computational details and overview of clinical data .

Published
2023
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38. Supplementary Text for: Genetic risk score Mendelian randomization shows that obesity measured as body mass index, but not waist:hip ratio, is causal for endometrial cancer from Genetic Risk Score Mendelian Randomization Shows that Obesity Measured as Body Mass Index, but not Waist:Hip Ratio, Is Causal for Endometrial Cancer

Author

Amanda B. Spurdle, Deborah J. Thompson, Douglas F. Easton, Alison M. Dunning, Ian Tomlinson, Paul D.P. Pharoah, Angela Cox, Vessela N. Kristensen, Graham G. Giles, Fergus J. Couch, Jenny Chang-Claude, Annika Lindblom, Hiltrud Brauch, Alfons Meindl, Hermann Brenner, Barbara Burwinkel, Anthony J. Swerdlow, Julian Peto, John L. Hopper, Jonathan P. Tyrer, Qin Wang, Kyriaki Michailidou, Manjeet K. Bolla, Emma Tham, Miriam Mints, Anthony Proietto, Geoffrey Otton, Katie A. Ashton, Henrica M.J. Werner, Jone Trovik, Helga B. Salvesen, Tormund S. Njølstad, Stacey J. Winham, Brooke L. Fridley, Ellen L. Goode, Sean C. Dowdy, Julie M. Cunningham, Patrick Neven, Diether Lambrechts, Jeroen Depreeuw, Daniela Annibali, Frederic Amant, Ingo B. Runnebaum, Peter Hillemanns, Matthias Dürst, Thilo Dörk, Jingmei Li, Per Hall, Hatef Darabi, Kamila Czene, Matthias Rübner, Alexander Hein, Peter A. Fasching, Arif B. Ekici, Matthias W. Beckmann, Shirley V. Hodgson, Lynn Martin, Maggie Gorman, Mitul Shah, Catherine S. Healey, Shahana Ahmed, Rodney J. Scott, Mark McEvoy, Elizabeth G. Holliday, Joe Dennis, Timothy Cheng, Sarah E. Medland, John Attia, Penelope M. Webb, Louise Marquart, Tracy A. O'Mara, and Jodie N. Painter

Abstract

Supplementary Text for manuscript.

Published
2023
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39. Supplementary Figure 1 from TP53 Mutation Spectrum in Breast Cancer Is Subtype Specific and Has Distinct Prognostic Relevance

Author

Anita Langerød, Carlos Caldas, Anne-Lise Børresen-Dale, Samuel Aparicio, Vessela N. Kristensen, David A. Quigley, Tomo Osako, Steven McKinney, Oscar M. Rueda, Suet-Feung Chin, Hans Kristian Moen Vollan, and Laxmi Silwal-Pandit

Abstract

PDF file - 21KB, Figure S1. Flow diagram showing the number of patients included in the study.

Published
2023
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40. Supplementary Figure 7 from TP53 Mutation Spectrum in Breast Cancer Is Subtype Specific and Has Distinct Prognostic Relevance

Author

Anita Langerød, Carlos Caldas, Anne-Lise Børresen-Dale, Samuel Aparicio, Vessela N. Kristensen, David A. Quigley, Tomo Osako, Steven McKinney, Oscar M. Rueda, Suet-Feung Chin, Hans Kristian Moen Vollan, and Laxmi Silwal-Pandit

Abstract

PDF file - 38KB, Figure S7. Kaplan-Meier survival curves showing BCSS of TP53 mutations status in ICs. Numbers at risk are listed below each chart (e = no. of Breast Cancer specific deaths).

Published
2023
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41. Supplementary Figure 3 from TP53 Mutation Spectrum in Breast Cancer Is Subtype Specific and Has Distinct Prognostic Relevance

Author

Anita Langerød, Carlos Caldas, Anne-Lise Børresen-Dale, Samuel Aparicio, Vessela N. Kristensen, David A. Quigley, Tomo Osako, Steven McKinney, Oscar M. Rueda, Suet-Feung Chin, Hans Kristian Moen Vollan, and Laxmi Silwal-Pandit

Abstract

PDF file - 264KB, Figure S3. A. Distribution of TP53 mutation, LOH, mRNA and protein expression in IC and PAM50 subtypes. B. Boxplot diagram showing the TP53 mRNA expression levels (log intensity values) with respect to TP53 protein expression (percentage of IHC staining) in mutated and wildtype tumors, and with respect to mutation types. The bold black line represents the mean TP53 expression value and the box represents 25th and 75th percentiles.

Published
2023
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42. Supplementary Table 2 from GSTP1 Promoter Haplotypes Affect DNA Methylation Levels and Promoter Activity in Breast Carcinomas

Author

Vessela N. Kristensen, Odd S. Gabrielsen, Anne-Lise Børresen-Dale, Per Eystein Lønning, Ivo G. Gut, Zohar Yakhini, Elen M. Brendeford, Fredrik E. Johansen, Grethe I.G. Alnaes, Hiroko K. Solvang, Jörg Tost, and Jo Anders Rønneberg

Abstract

Supplementary Table 2 from GSTP1 Promoter Haplotypes Affect DNA Methylation Levels and Promoter Activity in Breast Carcinomas

Published
2023
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43. Supplementary Table 1 from GSTP1 Promoter Haplotypes Affect DNA Methylation Levels and Promoter Activity in Breast Carcinomas

Author

Vessela N. Kristensen, Odd S. Gabrielsen, Anne-Lise Børresen-Dale, Per Eystein Lønning, Ivo G. Gut, Zohar Yakhini, Elen M. Brendeford, Fredrik E. Johansen, Grethe I.G. Alnaes, Hiroko K. Solvang, Jörg Tost, and Jo Anders Rønneberg

Abstract

Supplementary Table 1 from GSTP1 Promoter Haplotypes Affect DNA Methylation Levels and Promoter Activity in Breast Carcinomas

Published
2023
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44. Data from GSTP1 Promoter Haplotypes Affect DNA Methylation Levels and Promoter Activity in Breast Carcinomas

Author

Vessela N. Kristensen, Odd S. Gabrielsen, Anne-Lise Børresen-Dale, Per Eystein Lønning, Ivo G. Gut, Zohar Yakhini, Elen M. Brendeford, Fredrik E. Johansen, Grethe I.G. Alnaes, Hiroko K. Solvang, Jörg Tost, and Jo Anders Rønneberg

Abstract

The CpG island spanning the transcription start of the glutathione S-transferase P1 becomes methylated in a variety of human cancers including breast cancer. To study the effect of sequence variation on hypermethylation of the GSTP1 promoter, we analyzed the genetic and epigenetic variability in 90 tumors from patients with locally advanced breast cancer. High-resolution quantitative analysis revealed large variability in the DNA methylation levels. Lack of methylation was more often observed in the basal and normal-like estrogen receptor (ER)-negative tumors, and methylated GSTP1 was associated with better overall survival (P = 0.00063). Studies of the genetic variation identified 14 different haplotypes. The distribution of methylation levels of tumors homozygous for the most frequent haplotype was significantly different from other haplotype combinations (P = 0.011), the difference being more pronounced in ER-positive (P = 0.005) and progesterone receptor–positive (P = 0.008) tumors. Regression modeling identified the ER status and haplotype as the main determinants of DNA methylation variability. We identified a putative c-Myb response element (MRE) that was present in one of two minimal promoter haplotypes. In vitro analysis showed that c-Myb binds to the MRE, but binding was weakened by the two polymorphisms. Transient cotransfections in luminal-type and basal-like breast cancer cell lines confirmed cell-specific differential binding of c-Myb to the polymorphic sites, leading to a change in the expression from the GSTP1 promoter in vivo. GSTP1 expression was moderately but significantly (P = 0.01) reduced after siRNA-mediated knockdown of c-Myb. Our results indicate that haplotype structure of a promoter is important for the extent of DNA methylation. [Cancer Res 2008;68(14):5562–71]

Published
2023
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45. Supplementary Table 2 from Novel Associations between Common Breast Cancer Susceptibility Variants and Risk-Predicting Mammographic Density Measures

Author

Celine M. Vachon, John L. Hopper, V. Shane Pankratz, Peter A. Fasching, Douglas F. Easton, Alison M. Dunning, Paul Pharoah, Jacques Simard, Joe Dennis, Kyriaki Michailidou, Qin Wang, Manjeet K. Bolla, Inger Torhild Gram, Anne-Lise Borresen-Dale, Vessela N. Kristensen, Grethe I. Grenaker Alnaes, Giske Ursin, Julia A. Knight, Irene L. Andrulis, Carmel Apicella, Melissa C. Southey, Kavitha Krishnan, Laura Baglietto, Graham G. Giles, Christopher Haiman, Gertraud Maskarinec, Christy Woolcott, Laurence N. Kolonel, Loic Le Marchand, Mark A. Helvie, Heang-Ping Chan, Kaanan P. Shah, Julie A. Douglas, Matthias W. Beckmann, Katharina Heusinger, Sebastian M. Jud, Nicholas J. Wareham, Paula Smith, Kay-Tee Khaw, Robert N. Luben, Ruth M.L. Warren, Jean Leyland, Judith Brown, Fergus J. Couch, Kristen Purrington, Janet E. Olson, Julie Cunningham, Matt Jensen, Per Hall, Kamila Czene, Louise Eriksson, Jingmei Li, Aditi Hazra, Peter Kraft, Sara Lindstrom, Rulla M. Tamimi, Christopher Scott, Isabel dos Santos Silva, Deborah J. Thompson, and Jennifer Stone

Abstract

Supplementary Table 2. Associations (95% Confidence Interval (CI) in brackets) between all 77 breast cancer susceptibility variants and percent dense area, dense area, and non-dense area

Published
2023
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46. Supplementary Table 3 from Novel Associations between Common Breast Cancer Susceptibility Variants and Risk-Predicting Mammographic Density Measures

Author

Celine M. Vachon, John L. Hopper, V. Shane Pankratz, Peter A. Fasching, Douglas F. Easton, Alison M. Dunning, Paul Pharoah, Jacques Simard, Joe Dennis, Kyriaki Michailidou, Qin Wang, Manjeet K. Bolla, Inger Torhild Gram, Anne-Lise Borresen-Dale, Vessela N. Kristensen, Grethe I. Grenaker Alnaes, Giske Ursin, Julia A. Knight, Irene L. Andrulis, Carmel Apicella, Melissa C. Southey, Kavitha Krishnan, Laura Baglietto, Graham G. Giles, Christopher Haiman, Gertraud Maskarinec, Christy Woolcott, Laurence N. Kolonel, Loic Le Marchand, Mark A. Helvie, Heang-Ping Chan, Kaanan P. Shah, Julie A. Douglas, Matthias W. Beckmann, Katharina Heusinger, Sebastian M. Jud, Nicholas J. Wareham, Paula Smith, Kay-Tee Khaw, Robert N. Luben, Ruth M.L. Warren, Jean Leyland, Judith Brown, Fergus J. Couch, Kristen Purrington, Janet E. Olson, Julie Cunningham, Matt Jensen, Per Hall, Kamila Czene, Louise Eriksson, Jingmei Li, Aditi Hazra, Peter Kraft, Sara Lindstrom, Rulla M. Tamimi, Christopher Scott, Isabel dos Santos Silva, Deborah J. Thompson, and Jennifer Stone

Abstract

Supplementary Table 3. Associations (95% Confidence Interval (CI) in brackets) between breast cancer susceptibility variants and the mammographic measures with adjustment for case-control status

Published
2023
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47. Supplementary Table 1 from Novel Associations between Common Breast Cancer Susceptibility Variants and Risk-Predicting Mammographic Density Measures

Author

Celine M. Vachon, John L. Hopper, V. Shane Pankratz, Peter A. Fasching, Douglas F. Easton, Alison M. Dunning, Paul Pharoah, Jacques Simard, Joe Dennis, Kyriaki Michailidou, Qin Wang, Manjeet K. Bolla, Inger Torhild Gram, Anne-Lise Borresen-Dale, Vessela N. Kristensen, Grethe I. Grenaker Alnaes, Giske Ursin, Julia A. Knight, Irene L. Andrulis, Carmel Apicella, Melissa C. Southey, Kavitha Krishnan, Laura Baglietto, Graham G. Giles, Christopher Haiman, Gertraud Maskarinec, Christy Woolcott, Laurence N. Kolonel, Loic Le Marchand, Mark A. Helvie, Heang-Ping Chan, Kaanan P. Shah, Julie A. Douglas, Matthias W. Beckmann, Katharina Heusinger, Sebastian M. Jud, Nicholas J. Wareham, Paula Smith, Kay-Tee Khaw, Robert N. Luben, Ruth M.L. Warren, Jean Leyland, Judith Brown, Fergus J. Couch, Kristen Purrington, Janet E. Olson, Julie Cunningham, Matt Jensen, Per Hall, Kamila Czene, Louise Eriksson, Jingmei Li, Aditi Hazra, Peter Kraft, Sara Lindstrom, Rulla M. Tamimi, Christopher Scott, Isabel dos Santos Silva, Deborah J. Thompson, and Jennifer Stone

Abstract

Supplementary Table 1. Mean (95% Confidence Interval (CI)) mammographic density measurements by covariate distribution

Published
2023
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48. Supplementary Figure 1 from GSTP1 Promoter Haplotypes Affect DNA Methylation Levels and Promoter Activity in Breast Carcinomas

Author

Vessela N. Kristensen, Odd S. Gabrielsen, Anne-Lise Børresen-Dale, Per Eystein Lønning, Ivo G. Gut, Zohar Yakhini, Elen M. Brendeford, Fredrik E. Johansen, Grethe I.G. Alnaes, Hiroko K. Solvang, Jörg Tost, and Jo Anders Rønneberg

Abstract

Supplementary Figure 1 from GSTP1 Promoter Haplotypes Affect DNA Methylation Levels and Promoter Activity in Breast Carcinomas

Published
2023
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49. Supplementary Table 4 from GSTP1 Promoter Haplotypes Affect DNA Methylation Levels and Promoter Activity in Breast Carcinomas

Author

Vessela N. Kristensen, Odd S. Gabrielsen, Anne-Lise Børresen-Dale, Per Eystein Lønning, Ivo G. Gut, Zohar Yakhini, Elen M. Brendeford, Fredrik E. Johansen, Grethe I.G. Alnaes, Hiroko K. Solvang, Jörg Tost, and Jo Anders Rønneberg

Abstract

Supplementary Table 4 from GSTP1 Promoter Haplotypes Affect DNA Methylation Levels and Promoter Activity in Breast Carcinomas

Published
2023
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50. Supplementary Table 3 from GSTP1 Promoter Haplotypes Affect DNA Methylation Levels and Promoter Activity in Breast Carcinomas

Author

Vessela N. Kristensen, Odd S. Gabrielsen, Anne-Lise Børresen-Dale, Per Eystein Lønning, Ivo G. Gut, Zohar Yakhini, Elen M. Brendeford, Fredrik E. Johansen, Grethe I.G. Alnaes, Hiroko K. Solvang, Jörg Tost, and Jo Anders Rønneberg

Abstract

Supplementary Table 3 from GSTP1 Promoter Haplotypes Affect DNA Methylation Levels and Promoter Activity in Breast Carcinomas

Published
2023
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