Jonas Bergh

70.3k total citations · 14 hit papers
521 papers, 29.1k citations indexed

About

Jonas Bergh is a scholar working on Oncology, Cancer Research and Molecular Biology. According to data from OpenAlex, Jonas Bergh has authored 521 papers receiving a total of 29.1k indexed citations (citations by other indexed papers that have themselves been cited), including 337 papers in Oncology, 232 papers in Cancer Research and 136 papers in Molecular Biology. Recurrent topics in Jonas Bergh's work include Breast Cancer Treatment Studies (168 papers), HER2/EGFR in Cancer Research (93 papers) and Cancer Treatment and Pharmacology (86 papers). Jonas Bergh is often cited by papers focused on Breast Cancer Treatment Studies (168 papers), HER2/EGFR in Cancer Research (93 papers) and Cancer Treatment and Pharmacology (86 papers). Jonas Bergh collaborates with scholars based in Sweden, United States and United Kingdom. Jonas Bergh's co-authors include Lars Holmberg, Hans Nordgren, Johanna Smeds, Mauro Delorenzi, Martine Piccart, Lance D. Miller, Per Hall, Yudi Pawitan, Christos Sotiriou and Fátima Cardoso and has published in prestigious journals such as New England Journal of Medicine, Cell and Proceedings of the National Academy of Sciences.

In The Last Decade

Jonas Bergh

510 papers receiving 28.4k citations

Hit Papers

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What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Gene Expression Profiling in Breast Cancer: Understanding the Molecular Basis of Histologic Grade To Improve Prognosis

2006 1.5k citations Christos Sotiriou, Johanna Smeds et al. profile →
20-Year Risks of Breast-Cancer Recurrence after Stopping Endocrine Therapy at 5 Years

2017 1.1k citations Jonas Bergh et al. profile →
An expression signature for p53 status in human breast cancer predicts mutation status, transcriptional effects, and patient survival

2005 966 citations Lance D. Miller, Johanna Smeds et al. Proceedings of the National Academy of Sciences profile →
Validation and Clinical Utility of a 70-Gene Prognostic Signature for Women With Node-Negative Breast Cancer

2006 842 citations Jonas Bergh, Christos Sotiriou et al. profile →
Strong Time Dependence of the 76-Gene Prognostic Signature for Node-Negative Breast Cancer Patients in the TRANSBIG Multicenter Independent Validation Series

2007 726 citations Jonas Bergh, John A. Foekens et al. Clinical Cancer Research profile →
Gene expression profiling spares early breast cancer patients from adjuvant therapy: derived and validated in two population-based cohorts

2005 613 citations Yudi Pawitan, Per Hall et al. profile →
Definition of Clinically Distinct Molecular Subtypes in Estrogen Receptor–Positive Breast Carcinomas Through Genomic Grade

2007 599 citations Jonas Bergh, John A. Foekens et al. profile →
Identification of molecular apocrine breast tumours by microarray analysis

2005 585 citations Jonas Bergh et al. profile →
Spatially and functionally distinct subclasses of breast cancer-associated fibroblasts revealed by single cell RNA sequencing

2018 561 citations Michael Bartoschek, Matteo Bocci et al. profile →
Genetic Reclassification of Histologic Grade Delineates New Clinical Subtypes of Breast Cancer

2006 543 citations Anna V. Ivshina, Joshy George et al. Cancer Research profile →
Estimating the Risks of Breast Cancer Radiotherapy: Evidence From Modern Radiation Doses to the Lungs and Heart and From Previous Randomized Trials

2017 536 citations Jonas Bergh et al. profile →
Adjuvant bisphosphonate treatment in early breast cancer: meta-analyses of individual patient data from randomised trials

2015 473 citations Jonas Bergh profile →
An HIF-1α/VEGF-A Axis in Cytotoxic T Cells Regulates Tumor Progression

2017 408 citations Helene Rundqvist, Thomas Hatschek et al. profile →
Event-free survival by residual cancer burden with pembrolizumab in early-stage TNBC: exploratory analysis from KEYNOTE-522

2024 47 citations Lajos Pusztai, Carsten Denkert et al. Annals of Oncology profile →

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author						Papers	Cites
Jonas Bergh	15.6k	11.6k	11.2k	4.9k	3.3k	521	29.1k
Lars A. Akslen	14.0k 0.9×	11.6k 1.0×	13.8k 1.2×	4.4k 0.9×	3.3k 1.0×	314	29.1k
Christos Sotiriou	15.6k 1.0×	13.8k 1.2×	12.3k 1.1×	4.4k 0.9×	2.9k 0.9×	366	29.1k
Ana M. González-Angulo	15.2k 1.0×	11.4k 1.0×	8.9k 0.8×	5.2k 1.0×	2.6k 0.8×	281	25.2k
Stephen B. Fox	13.0k 0.8×	10.4k 0.9×	14.2k 1.3×	4.7k 1.0×	2.9k 0.9×	423	29.3k
Carsten Denkert	15.3k 1.0×	11.3k 1.0×	11.5k 1.0×	4.4k 0.9×	1.9k 0.6×	522	29.5k
Lajos Pusztai	20.1k 1.3×	16.9k 1.5×	12.1k 1.1×	5.7k 1.2×	2.9k 0.9×	591	35.0k
Torsten O. Nielsen	16.1k 1.0×	14.5k 1.2×	10.3k 0.9×	7.7k 1.6×	3.3k 1.0×	241	31.1k
W. Fraser Symmans	14.5k 0.9×	14.3k 1.2×	8.2k 0.7×	3.9k 0.8×	2.4k 0.7×	323	25.7k
Lisa A. Carey	16.1k 1.0×	12.1k 1.0×	7.3k 0.7×	5.4k 1.1×	3.1k 0.9×	421	25.9k
Fátima Cardoso	13.8k 0.9×	10.5k 0.9×	7.4k 0.7×	4.9k 1.0×	3.2k 0.9×	403	25.4k

Countries citing papers authored by Jonas Bergh

Since Specialization

Citations

This map shows the geographic impact of Jonas Bergh's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Jonas Bergh with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jonas Bergh more than expected).

Fields of papers citing papers by Jonas Bergh

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jonas Bergh. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Jonas Bergh. The network helps show where Jonas Bergh may publish in the future.

Co-authorship network of co-authors of Jonas Bergh

This figure shows the co-authorship network connecting the top 25 collaborators of Jonas Bergh. A scholar is included among the top collaborators of Jonas Bergh based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Jonas Bergh. Jonas Bergh is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Liu, Xingrong, Davide Massa, Nikos Tsiknakis, et al.. (2025). Prognostic significance of tumour Ki-67 dynamics during neoadjuvant treatment in patients with breast cancer: a population-based cohort study. The Lancet Regional Health - Europe. 58. 101432–101432.

Pusztai, Lajos, Carsten Denkert, Joyce O’Shaughnessy, et al.. (2024). Event-free survival by residual cancer burden with pembrolizumab in early-stage TNBC: exploratory analysis from KEYNOTE-522. Annals of Oncology. 35(5). 429–436. 47 indexed citations breakdown →

Karakatsanis, Andreas, Anne Andersson, Zakaria Einbeigi, et al.. (2024). 339TiP A randomized trial of trastuzumab deruxtecan and biology-driven selection of neoadjuvant treatment for HER2-positive breast cancer (ARIADNE). Annals of Oncology. 35. S356–S356. 1 indexed citations

Anandavadivelan, Poorna, Sara Mijwel, Maria Wiklander, et al.. (2024). Five-year follow-up of the OptiTrain trial on concurrent resistance and high-intensity interval training during chemotherapy for patients with breast cancer. Scientific Reports. 14(1). 15333–15333. 4 indexed citations

Altena, Renske, Antonios Tzortzakakis, Thuy Tran, et al.. (2023). Current status of contemporary diagnostic radiotracers in the management of breast cancer: first steps toward theranostic applications. EJNMMI Research. 13(1). 43–43. 8 indexed citations

Strell, Carina, Erik Holmberg, Per Karlsson, et al.. (2021). High PDGFRb Expression Predicts Resistance to Radiotherapy in DCIS within the SweDCIS Randomized Trial. Clinical Cancer Research. 27(12). 3469–3477. 10 indexed citations

Matikas, Alexios, Ioannis Zerdes, John Lövrot, et al.. (2019). Prognostic Implications of PD-L1 Expression in Breast Cancer: Systematic Review and Meta-analysis of Immunohistochemistry and Pooled Analysis of Transcriptomic Data. Clinical Cancer Research. 25(18). 5717–5726. 73 indexed citations

Tobin, Nicholas P., Arian Lundberg, Linda S. Lindström, et al.. (2017). PAM50 Provides Prognostic Information When Applied to the Lymph Node Metastases of Advanced Breast Cancer Patients. Clinical Cancer Research. 23(23). 7225–7231. 10 indexed citations

Lundberg, Arian, Linda S. Lindström, J. Chuck Harrell, et al.. (2017). Gene Expression Signatures and Immunohistochemical Subtypes Add Prognostic Value to Each Other in Breast Cancer Cohorts. Clinical Cancer Research. 23(24). 7512–7520. 34 indexed citations

10.

Tzogani, Kyriaki, Paula B. van Hennik, Jan Sjöberg, et al.. (2017). EMA Review of Panobinostat (Farydak) for the Treatment of Adult Patients with Relapsed and/or Refractory Multiple Myeloma. The Oncologist. 23(5). 631–636. 31 indexed citations

11.

Bartish, Margarita, Jeanette Östling, Artur Mezheyeuski, et al.. (2016). Guidance Molecule SEMA3A Restricts Tumor Growth by Differentially Regulating the Proliferation of Tumor-Associated Macrophages. Cancer Research. 76(11). 3166–3178. 46 indexed citations

12.

Brand, Judith S., Elham Hedayati, Keith Humphreys, et al.. (2016). Chemotherapy, Genetic Susceptibility, and Risk of Venous Thromboembolism in Breast Cancer Patients. Clinical Cancer Research. 22(21). 5249–5255. 11 indexed citations

13.

Li, Jingmei, Emma Ivansson, Daniel Klevebring, et al.. (2016). Molecular Differences between Screen-Detected and Interval Breast Cancers Are Largely Explained by PAM50 Subtypes. Clinical Cancer Research. 23(10). 2584–2592. 15 indexed citations

14.

Tobin, Nicholas P., Kristian Wennmalm, Linda S. Lindström, et al.. (2016). An Endothelial Gene Signature Score Predicts Poor Outcome in Patients with Endocrine-Treated, Low Genomic Grade Breast Tumors. Clinical Cancer Research. 22(10). 2417–2426. 7 indexed citations

15.

Cunha, Sara I., Matteo Bocci, John Lövrot, et al.. (2015). Endothelial ALK1 Is a Therapeutic Target to Block Metastatic Dissemination of Breast Cancer. Cancer Research. 75(12). 2445–2456. 48 indexed citations

16.

Eschrich, Steven A., William J. Fulp, Yudi Pawitan, et al.. (2012). Validation of a Radiosensitivity Molecular Signature in Breast Cancer. Clinical Cancer Research. 18(18). 5134–5143. 145 indexed citations

17.

Miller, Lance D., Lan Coffman, Jeff W. Chou, et al.. (2011). An Iron Regulatory Gene Signature Predicts Outcome in Breast Cancer. Cancer Research. 71(21). 6728–6737. 169 indexed citations

18.

Frasor, Jonna, Edmund C. Chang, Barry S. Komm, et al.. (2006). Gene Expression Preferentially Regulated by Tamoxifen in Breast Cancer Cells and Correlations with Clinical Outcome. Cancer Research. 66(14). 7334–7340. 137 indexed citations

19.

Ivshina, Anna V., Joshy George, O. V. Sen’ko, et al.. (2006). Genetic Reclassification of Histologic Grade Delineates New Clinical Subtypes of Breast Cancer. Cancer Research. 66(21). 10292–10301. 543 indexed citations breakdown →

20.

Miller, Lance D., Johanna Smeds, Joshy George, et al.. (2005). An expression signature for p53 status in human breast cancer predicts mutation status, transcriptional effects, and patient survival. Proceedings of the National Academy of Sciences. 102(38). 13550–13555. 966 indexed citations breakdown →

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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