Michael T. Lewis

13.7k total citations · 2 hit papers
110 papers, 6.5k citations indexed

About

Michael T. Lewis is a scholar working on Molecular Biology, Oncology and Cancer Research. According to data from OpenAlex, Michael T. Lewis has authored 110 papers receiving a total of 6.5k indexed citations (citations by other indexed papers that have themselves been cited), including 66 papers in Molecular Biology, 66 papers in Oncology and 28 papers in Cancer Research. Recurrent topics in Michael T. Lewis's work include Cancer Cells and Metastasis (50 papers), Hedgehog Signaling Pathway Studies (21 papers) and Epigenetics and DNA Methylation (20 papers). Michael T. Lewis is often cited by papers focused on Cancer Cells and Metastasis (50 papers), Hedgehog Signaling Pathway Studies (21 papers) and Epigenetics and DNA Methylation (20 papers). Michael T. Lewis collaborates with scholars based in United States, Canada and United Kingdom. Michael T. Lewis's co-authors include Jenny C. Chang, Gary C. Chamness, Jeffrey M. Rosen, C. Kent Osborne, S. G. Hilsenbeck, Susan G. Hilsenbeck, Anne C. Pavlick, Hiu Yung Wong, Xiaolei Zhang and Jian Huang and has published in prestigious journals such as Nature, Proceedings of the National Academy of Sciences and Nature Communications.

In The Last Decade

Michael T. Lewis

105 papers receiving 6.4k citations

Hit Papers

Intrinsic Resistance of Tumorigenic Breast Cancer Cells t... 2008 2026 2014 2020 2008 2017 400 800 1.2k

Peers

Michael T. Lewis
Xiaojiang Cui United States
Alana L. Welm United States
Pepper Schedin United States
Venu Raman United States
Brian Bierie United States
Asha S. Multani United States
Hui‐Wen Lo United States
Xiaojiang Cui United States
Michael T. Lewis
Citations per year, relative to Michael T. Lewis Michael T. Lewis (= 1×) peers Xiaojiang Cui

Countries citing papers authored by Michael T. Lewis

Since Specialization
Citations

This map shows the geographic impact of Michael T. Lewis'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 Michael T. Lewis with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michael T. Lewis more than expected).

Fields of papers citing papers by Michael T. Lewis

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Michael T. Lewis. 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 Michael T. Lewis. The network helps show where Michael T. Lewis may publish in the future.

Co-authorship network of co-authors of Michael T. Lewis

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

All Works

20 of 20 papers shown
1.
2.
Dobrolecki, Lacey E., Xudong Zhang, Li Yang, et al.. (2024). UBA1 inhibition sensitizes cancer cells to PARP inhibitors. Cell Reports Medicine. 5(12). 101834–101834. 4 indexed citations
3.
Gou, Xuxu, Meenakshi Anurag, Jonathan T. Lei, et al.. (2023). Kinome Reprogramming Is a Targetable Vulnerability in ESR1 Fusion-Driven Breast Cancer. Cancer Research. 83(19). 3237–3251. 4 indexed citations
4.
Malyarenko, Dariya, Stephen Pickup, Rong Zhou, et al.. (2023). Evaluation of Apparent Diffusion Coefficient Repeatability and Reproducibility for Preclinical MRIs Using Standardized Procedures and a Diffusion-Weighted Imaging Phantom. Tomography. 9(1). 375–386. 4 indexed citations
5.
Wang, Wei, Qinbo Cai, Tao Shen, et al.. (2022). MAPK4 promotes triple negative breast cancer growth and reduces tumor sensitivity to PI3K blockade. Nature Communications. 13(1). 245–245. 35 indexed citations
6.
Dobrolecki, Lacey E., Matthew H. Bailey, Alana L. Welm, et al.. (2022). Immunologically “cold” triple negative breast cancers engraft at a higher rate in patient derived xenografts. npj Breast Cancer. 8(1). 104–104. 9 indexed citations
7.
Menghi, Francesca, Kalyan Banda, Pooja Kumar, et al.. (2022). Genomic and epigenomic BRCA alterations predict adaptive resistance and response to platinum-based therapy in patients with triple-negative breast and ovarian carcinomas. Science Translational Medicine. 14(652). eabn1926–eabn1926. 33 indexed citations
8.
Gao, Yang, Elena B. Kabotyanski, Jonathan H. Shepherd, et al.. (2021). Tumor Suppressor PLK2 May Serve as a Biomarker in Triple-Negative Breast Cancer for Improved Response to PLK1 Therapeutics. Cancer Research Communications. 1(3). 178–193. 8 indexed citations
9.
Landua, John D., Ricardo Moraes, Ellen M. Carpenter, & Michael T. Lewis. (2020). Hoxd10 Is Required Systemically for Secretory Activation in Lactation and Interacts Genetically with Hoxd9. Journal of Mammary Gland Biology and Neoplasia. 25(2). 145–162.
10.
Franklin, Derek A., Joe T. Sharick, Paula I. González-Ericsson, et al.. (2020). MEK activation modulates glycolysis and supports suppressive myeloid cells in TNBC. JCI Insight. 5(15). 25 indexed citations
11.
Gómez‐Miragaya, Jorge, Sebastián Morán, María Eréndira Calleja-Cervantes, et al.. (2019). The Altered Transcriptome and DNA Methylation Profiles of Docetaxel Resistance in Breast Cancer PDX Models. Molecular Cancer Research. 17(10). 2063–2076. 24 indexed citations
12.
Bu, Wen, Zhenyu Liu, Weiyu Jiang, et al.. (2018). Mammary Precancerous Stem and Non-Stem Cells Evolve into Cancers of Distinct Subtypes. Cancer Research. 79(1). 61–71. 31 indexed citations
13.
Schott, Anne F., Melissa D. Landis, Gabriela Dontu, et al.. (2013). Preclinical and Clinical Studies of Gamma Secretase Inhibitors with Docetaxel on Human Breast Tumors. Clinical Cancer Research. 19(6). 1512–1524. 209 indexed citations
14.
Iyer, Vandana, Ina Klebba, Jessica McCready, et al.. (2012). Estrogen Promotes ER-Negative Tumor Growth and Angiogenesis through Mobilization of Bone Marrow–Derived Monocytes. Cancer Research. 72(11). 2705–2713. 42 indexed citations
15.
Selever, Jennifer, Guowei Gu, Michael T. Lewis, et al.. (2011). Dicer-Mediated Upregulation of BCRP Confers Tamoxifen Resistance in Human Breast Cancer Cells. Clinical Cancer Research. 17(20). 6510–6521. 45 indexed citations
16.
Litzenburger, Beate C., Chad J. Creighton, Anna Tsimelzon, et al.. (2010). High IGF-IR Activity in Triple-Negative Breast Cancer Cell Lines and Tumorgrafts Correlates with Sensitivity to Anti–IGF-IR Therapy. Clinical Cancer Research. 17(8). 2314–2327. 109 indexed citations
17.
Herynk, Matthew H., Michael T. Lewis, Torsten Hopp, et al.. (2009). Accelerated mammary maturation and differentiation, and delayed MMTVneu-induced tumorigenesis of K303R mutant ERα transgenic mice. Oncogene. 28(36). 3177–3187. 4 indexed citations
18.
Lewis, Michael T., Jian Huang, Carolina Gutiérrez, et al.. (2008). Intrinsic Resistance of Tumorigenic Breast Cancer Cells to Chemotherapy. JNCI Journal of the National Cancer Institute. 100(9). 672–679. 1418 indexed citations breakdown →
20.
Lewis, Michael T., Sarajane Ross, Phyllis Strickland, et al.. (2001). The Gli2 Transcription Factor Is Required for Normal Mouse Mammary Gland Development. Developmental Biology. 238(1). 133–144. 84 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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