Dane Cheasley

953 total citations
19 papers, 276 citations indexed

About

Dane Cheasley is a scholar working on Oncology, Molecular Biology and Cancer Research. According to data from OpenAlex, Dane Cheasley has authored 19 papers receiving a total of 276 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Oncology, 9 papers in Molecular Biology and 7 papers in Cancer Research. Recurrent topics in Dane Cheasley's work include Genetic factors in colorectal cancer (5 papers), Cancer Genomics and Diagnostics (5 papers) and Ovarian cancer diagnosis and treatment (5 papers). Dane Cheasley is often cited by papers focused on Genetic factors in colorectal cancer (5 papers), Cancer Genomics and Diagnostics (5 papers) and Ovarian cancer diagnosis and treatment (5 papers). Dane Cheasley collaborates with scholars based in Australia, Canada and United Kingdom. Dane Cheasley's co-authors include Jordane Malaterre, Robert G. Ramsay, Lloyd Pereira, Elizabeth Vincan, Shienny Sampurno, Ian Campbell, Ryan Cross, Kylie L. Gorringe, Yvette Drabsch and Simone M. Rowley and has published in prestigious journals such as Journal of Clinical Oncology, JNCI Journal of the National Cancer Institute and Cancer Research.

In The Last Decade

Dane Cheasley

17 papers receiving 274 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Dane Cheasley Australia 11 166 124 85 57 49 19 276
Emmanuelle Barouk-Simonet France 4 160 1.0× 72 0.6× 59 0.7× 93 1.6× 70 1.4× 11 287
Simona Agata Italy 9 224 1.3× 142 1.1× 79 0.9× 167 2.9× 33 0.7× 12 378
Marianne Brodtkorb Eide Norway 5 89 0.5× 98 0.8× 102 1.2× 43 0.8× 109 2.2× 5 247
T Noguchi France 7 153 0.9× 90 0.7× 135 1.6× 127 2.2× 121 2.5× 9 317
Veli-Matti Kosma Finland 6 250 1.5× 115 0.9× 79 0.9× 51 0.9× 87 1.8× 6 393
Susana Bizarro Portugal 12 165 1.0× 63 0.5× 66 0.8× 58 1.0× 47 1.0× 29 335
Elena V. Preobrazhenskaya Russia 12 148 0.9× 141 1.1× 109 1.3× 149 2.6× 82 1.7× 42 359
Dmitry E. Matsko Russia 10 174 1.0× 117 0.9× 108 1.3× 157 2.8× 44 0.9× 24 349
Santiago Demajo Spain 9 281 1.7× 99 0.8× 60 0.7× 70 1.2× 54 1.1× 13 450
Jenny Varley United Kingdom 11 224 1.3× 194 1.6× 74 0.9× 71 1.2× 58 1.2× 14 378

Countries citing papers authored by Dane Cheasley

Since Specialization
Citations

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

Fields of papers citing papers by Dane Cheasley

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Dane Cheasley

This figure shows the co-authorship network connecting the top 25 collaborators of Dane Cheasley. A scholar is included among the top collaborators of Dane Cheasley 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 Dane Cheasley. Dane Cheasley is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

19 of 19 papers shown
1.
Cheasley, Dane, et al.. (2026). Breast cancers arising in high mammographic density tissue harbor tumor-promoting immune profiles. npj Breast Cancer. 12(1). 20–20.
2.
Pishas, Kathleen I., Simone McInerny, Simone M. Rowley, et al.. (2025). Assessment of candidate high-grade serous ovarian carcinoma predisposition genes through integrated germline and tumour sequencing. npj Genomic Medicine. 10(1). 1–1.
3.
Pishas, Kathleen I., Nikola A. Bowden, Ian Campbell, et al.. (2024). High-throughput drug screening identifies novel therapeutics for Low Grade Serous Ovarian Carcinoma. Scientific Data. 11(1). 1024–1024. 1 indexed citations
4.
Kang, Eun Young, Cheng‐Han Lee, Dane Cheasley, et al.. (2024). Molecular Surrogate Subtypes of Ovarian and Peritoneal Low-grade Serous Carcinoma. International Journal of Gynecological Pathology. 43(6). 617–625. 2 indexed citations
5.
Cheasley, Dane, Marta Llauradó Fernández, Martin Köbel, et al.. (2022). Molecular characterization of low-grade serous ovarian carcinoma identifies genomic aberrations according to hormone receptor expression. npj Precision Oncology. 6(1). 47–47. 13 indexed citations
6.
Li, Na, Ella R. Thompson, Simone McInerny, et al.. (2021). Investigation of monogenic causes of familial breast cancer: data from the BEACCON case-control study. npj Breast Cancer. 7(1). 76–76. 13 indexed citations
7.
Pishas, Kathleen I., Ahwan Pandey, Jessica A. Beach, et al.. (2021). Phenotypic Consequences of SLC25A40-ABCB1 Fusions beyond Drug Resistance in High-Grade Serous Ovarian Cancer. Cancers. 13(22). 5644–5644. 3 indexed citations
8.
Cheasley, Dane, Lisa Devereux, Siobhan Hughes, et al.. (2020). The TP53 mutation rate differs in breast cancers that arise in women with high or low mammographic density. npj Breast Cancer. 6(1). 34–34. 6 indexed citations
9.
Kader, Tanjina, Kenneth Elder, Magnus Zethoven, et al.. (2020). The genetic architecture of breast papillary lesions as a predictor of progression to carcinoma. npj Breast Cancer. 6(1). 9–9. 13 indexed citations
10.
Li, Na, Simone McInerny, Magnus Zethoven, et al.. (2019). Combined Tumor Sequencing and Case-Control Analyses of RAD51C in Breast Cancer. JNCI Journal of the National Cancer Institute. 111(12). 1332–1338. 23 indexed citations
11.
Cheasley, Dane, Robert N. Jorissen, Sheng Liu, et al.. (2018). Genomic approach to translational studies in colorectal cancer. Translational Cancer Research. 4(3). 235–255. 2 indexed citations
12.
Cheasley, Dane. (2018). Molecular comparison of interval and screen-detected breast cancers.. Journal of Clinical Oncology. 36(15_suppl). 12124–12124. 1 indexed citations
13.
Li, Na, Simone M. Rowley, Dane Cheasley, et al.. (2018). Molecular analysis of PALB2‐associated breast cancers. The Journal of Pathology. 245(1). 53–60. 23 indexed citations
14.
Cheasley, Dane, Lloyd Pereira, Shienny Sampurno, et al.. (2015). Defective Myb Function Ablates Cyclin E1 Expression and Perturbs Intestinal Carcinogenesis. Molecular Cancer Research. 13(8). 1185–1196. 14 indexed citations
15.
Malaterre, Jordane, Lloyd Pereira, Tracy L. Putoczki, et al.. (2015). Intestinal-specific activatable Myb initiates colon tumorigenesis in mice. Oncogene. 35(19). 2475–2484. 19 indexed citations
16.
Germann, Markus, Huiling Xu, Jordane Malaterre, et al.. (2014). Tripartite interactions between Wnt signaling, Notch and Myb for stem/progenitor cell functions during intestinal tumorigenesis. Stem Cell Research. 13(3). 355–366. 14 indexed citations
17.
Sampurno, Shienny, Dane Cheasley, Huiling Xu, et al.. (2013). The Myb-p300-CREB axis modulates intestine homeostasis, radiosensitivity and tumorigenesis. Cell Death and Disease. 4(4). e605–e605. 25 indexed citations
18.
Drabsch, Yvette, Ryan Cross, Dane Cheasley, et al.. (2011). MYB Is Essential for Mammary Tumorigenesis. Cancer Research. 71(22). 7029–7037. 55 indexed citations
19.
Cheasley, Dane, et al.. (2011). Myb Controls Intestinal Stem Cell Genes and Self-Renewal. Stem Cells. 29(12). 2042–2050. 49 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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