Sean Ferree

6.0k total citations · 1 hit paper
49 papers, 2.1k citations indexed

About

Sean Ferree is a scholar working on Cancer Research, Oncology and Pulmonary and Respiratory Medicine. According to data from OpenAlex, Sean Ferree has authored 49 papers receiving a total of 2.1k indexed citations (citations by other indexed papers that have themselves been cited), including 39 papers in Cancer Research, 25 papers in Oncology and 18 papers in Pulmonary and Respiratory Medicine. Recurrent topics in Sean Ferree's work include Breast Cancer Treatment Studies (32 papers), HER2/EGFR in Cancer Research (16 papers) and Advanced Breast Cancer Therapies (15 papers). Sean Ferree is often cited by papers focused on Breast Cancer Treatment Studies (32 papers), HER2/EGFR in Cancer Research (16 papers) and Advanced Breast Cancer Therapies (15 papers). Sean Ferree collaborates with scholars based in United States, United Kingdom and Denmark. Sean Ferree's co-authors include Ivana Šestak, Carl Schaper, Mitch Dowsett, Jack Cuzick, J. Wayne Cowens, Elena López‐Knowles, James J. Storhoff, Torsten O. Nielsen, Anita K. Dunbier and Naeem Dowidar and has published in prestigious journals such as Journal of Clinical Oncology, Blood and JNCI Journal of the National Cancer Institute.

In The Last Decade

Sean Ferree

48 papers receiving 2.1k citations

Hit Papers

align trajectories

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.

Comparison of PAM50 Risk of Recurrence Score With Oncotype DX and IHC4 for Predicting Risk of Distant Recurrence After Endocrine Therapy

2013 436 citations Mitch Dowsett, Ivana Šestak et al. Journal of Clinical Oncology profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Sean Ferree United States	17	1.6k	1.1k	597	536	444	49	2.1k
Wanseop Kim United States	4	1.5k 0.9×	1.0k 0.9×	462 0.8×	297 0.6×	427 1.0×	6	1.9k
Lila Zabaglo United Kingdom	16	1.4k 0.8×	1.2k 1.1×	421 0.7×	399 0.7×	305 0.7×	37	1.9k
Eva Carrasco Spain	22	1.1k 0.7×	1.5k 1.3×	358 0.6×	476 0.9×	302 0.7×	77	2.0k
MM Regan Switzerland	9	1.1k 0.7×	1.0k 0.9×	329 0.6×	294 0.5×	248 0.6×	32	1.7k
Peter Osin United Kingdom	15	1.8k 1.1×	1.3k 1.2×	1.1k 1.8×	735 1.4×	802 1.8×	39	2.8k
Véronique Becette France	18	983 0.6×	1.0k 0.9×	889 1.5×	412 0.8×	281 0.6×	56	2.2k
Anthony Skene United Kingdom	14	1.7k 1.0×	1.7k 1.5×	419 0.7×	551 1.0×	781 1.8×	28	2.4k
J. Zujewski United States	10	1.0k 0.6×	1.1k 1.0×	548 0.9×	290 0.5×	218 0.5×	16	1.9k
Anne‐Vibeke Lænkholm Denmark	30	1.2k 0.7×	1.2k 1.1×	1.2k 1.9×	461 0.9×	496 1.1×	120	2.6k

Countries citing papers authored by Sean Ferree

Since Specialization

Citations

This map shows the geographic impact of Sean Ferree'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 Sean Ferree with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sean Ferree more than expected).

Fields of papers citing papers by Sean Ferree

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Sean Ferree. 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 Sean Ferree. The network helps show where Sean Ferree may publish in the future.

Co-authorship network of co-authors of Sean Ferree

This figure shows the co-authorship network connecting the top 25 collaborators of Sean Ferree. A scholar is included among the top collaborators of Sean Ferree 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 Sean Ferree. Sean Ferree 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

Fitzal, Florian, Martin Filipits, Christian Fesl, et al.. (2021). PAM-50 predicts local recurrence after breast cancer surgery in postmenopausal patients with ER+/HER2– disease: results from 1204 patients in the randomized ABCSG-8 trial. British journal of surgery. 108(3). 308–314. 18 indexed citations

Buus, Richard, Ivana Šestak, Ralf Kronenwett, et al.. (2020). Molecular Drivers of Oncotype DX, Prosigna, EndoPredict, and the Breast Cancer Index: A TransATAC Study. Journal of Clinical Oncology. 39(2). 126–135. 62 indexed citations

Lænkholm, Anne‐Vibeke, Maj‐Britt Jensen, Jens Ole Eriksen, et al.. (2020). Population-based Study of Prosigna-PAM50 and Outcome Among Postmenopausal Women With Estrogen Receptor-positive and HER2-negative Operable Invasive Lobular or Ductal Breast Cancer. Clinical Breast Cancer. 20(4). e423–e432. 17 indexed citations

Jensen, Maj‐Britt, Anne‐Vibeke Lænkholm, Eva Balslev, et al.. (2020). The Prosigna 50-gene profile and responsiveness to adjuvant anthracycline-based chemotherapy in high-risk breast cancer patients. npj Breast Cancer. 6(1). 7–7. 17 indexed citations

Danaher, Patrick, et al.. (2019). A gene expression assay for simultaneous measurement of microsatellite instability and anti-tumor immune activity. Journal for ImmunoTherapy of Cancer. 7(1). 15–15. 20 indexed citations

Šestak, Ivana, Richard Buus, Jack Cuzick, et al.. (2018). Comparison of the Performance of 6 Prognostic Signatures for Estrogen Receptor–Positive Breast Cancer. JAMA Oncology. 4(4). 545–545. 204 indexed citations

Jensen, Maj‐Britt, Anne‐Vibeke Lænkholm, Torsten O. Nielsen, et al.. (2018). The Prosigna gene expression assay and responsiveness to adjuvant cyclophosphamide-based chemotherapy in premenopausal high-risk patients with breast cancer. Breast Cancer Research. 20(1). 79–79. 43 indexed citations

Lænkholm, Anne‐Vibeke, Maj‐Britt Jensen, Jens Ole Eriksen, et al.. (2017). The ability of PAM50 risk of recurrence score to predict 10-year distant recurrence in hormone receptor-positive postmenopausal women with special histological subtypes. Acta Oncologica. 57(1). 44–50. 11 indexed citations

Šestak, Ivana, Mitch Dowsett, Sean Ferree, Frederick L. Baehner, & Jack Cuzick. (2016). Retrospective analysis of molecular scores for the prediction of distant recurrence according to baseline risk factors. Breast Cancer Research and Treatment. 159(1). 71–78. 10 indexed citations

10.

Perez, Edith A., Karla V. Ballman, Afshin Mashadi-Hossein, et al.. (2016). Intrinsic Subtype and Therapeutic Response Among HER2-Positive Breaty st Tumors from the NCCTG (Alliance) N9831 Trial. JNCI Journal of the National Cancer Institute. 109(2). djw207–djw207. 27 indexed citations

11.

Prat, Aleix, Patricia Galván, Begoña Jiménez, et al.. (2015). Prediction of Response to Neoadjuvant Chemotherapy Using Core Needle Biopsy Samples with the Prosigna Assay. Clinical Cancer Research. 22(3). 560–566. 64 indexed citations

12.

Gnant, Michael, Ivana Šestak, Martin Filipits, et al.. (2015). Identifying clinically relevant prognostic subgroups of postmenopausal women with node-positive hormone receptor-positive early-stage breast cancer treated with endocrine therapy: a combined analysis of ABCSG-8 and ATAC using the PAM50 risk of recurrence score and intrinsic subtype. Annals of Oncology. 26(8). 1685–1691. 77 indexed citations

13.

Wallden, Brett, James J. Storhoff, Torsten O. Nielsen, et al.. (2015). Development and verification of the PAM50-based Prosigna breast cancer gene signature assay. BMC Medical Genomics. 8(1). 54–54. 337 indexed citations

14.

Šestak, Ivana, et al.. (2014). Analysis of Molecular Scores for the Prediction of Distant Recurrence According to Body Mass Index and Age at Baseline. Annals of Oncology. 25. i8–i8. 3 indexed citations

15.

Nielsen, Torsten O., Brett Wallden, Carl Schaper, et al.. (2014). Analytical validation of the PAM50-based Prosigna Breast Cancer Prognostic Gene Signature Assay and nCounter Analysis System using formalin-fixed paraffin-embedded breast tumor specimens. BMC Cancer. 14(1). 177–177. 211 indexed citations

16.

Gnant, Michael, Martin Filipits, Peter Dubsky, et al.. (2013). Predicting Risk for Late Metastasis: the PAM50 Risk of Recurrence (ROR) Score After 5 Years of Endocrine Therapy in Postmenopausal Women with Hr+ Early Breast Cancer: a Study On 1,478 Patients from the Abcsg-8 Trial. Annals of Oncology. 24. iii29–iii29. 15 indexed citations

17.

Šestak, Ivana, Mitch Dowsett, Dennis C. Sgroi, et al.. (2013). Comparison of Five Different Scores for the Prediction of Late Recurrence for Oestrogen Receptor-Positive Breast Cancer. Annals of Oncology. 24. iii29–iii29. 4 indexed citations

18.

Šestak, Ivana, Mitch Dowsett, Lila Zabaglo, et al.. (2013). Factors Predicting Late Recurrence for Estrogen Receptor–Positive Breast Cancer. JNCI Journal of the National Cancer Institute. 105(19). 1504–1511. 170 indexed citations

19.

Dowsett, Mitch, Elena López‐Knowles, Kiran Sidhu, et al.. (2011). S4-5: Comparison of PAM50 Risk of Recurrence (ROR) Score with OncotypeDx and IHC4 for Predicting Residual Risk of RFS and Distant-(D)RFS after Endocrine Therapy: A TransATAC Study.. Cancer Research. 71(24_Supplement). S4–5. 8 indexed citations

20.

Birditt, Brian, et al.. (2008). Direct multiplexed measurement of gene expression with color-coded probe pairs (vol 26, pg 317, 2008). UCL Discovery (University College London). 2 indexed citations

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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