Matthew J. Lazzara

2.4k total citations
51 papers, 1.5k citations indexed

About

Matthew J. Lazzara is a scholar working on Molecular Biology, Oncology and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Matthew J. Lazzara has authored 51 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 32 papers in Molecular Biology, 17 papers in Oncology and 7 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Matthew J. Lazzara's work include Protein Tyrosine Phosphatases (8 papers), HER2/EGFR in Cancer Research (7 papers) and Monoclonal and Polyclonal Antibodies Research (7 papers). Matthew J. Lazzara is often cited by papers focused on Protein Tyrosine Phosphatases (8 papers), HER2/EGFR in Cancer Research (7 papers) and Monoclonal and Polyclonal Antibodies Research (7 papers). Matthew J. Lazzara collaborates with scholars based in United States, India and Finland. Matthew J. Lazzara's co-authors include William M. Deen, Janine M. Buonato, Bryan D. Myers, Glen Bolton, Nisha Sosale, Douglas A. Lauffenburger, Austin W. Boesch, Alice M. Walsh, Deepak Nihalani and Daniel Blankschtein and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of Biological Chemistry and Molecular and Cellular Biology.

In The Last Decade

Matthew J. Lazzara

49 papers receiving 1.5k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Matthew J. Lazzara United States 22 805 319 275 169 163 51 1.5k
Yukiko Nakano Japan 25 1.1k 1.3× 122 0.4× 149 0.5× 121 0.7× 69 0.4× 85 2.0k
Toru Yoshida Japan 23 596 0.7× 268 0.8× 182 0.7× 200 1.2× 51 0.3× 134 2.1k
Yabo Zhou China 19 793 1.0× 469 1.5× 85 0.3× 222 1.3× 135 0.8× 39 1.7k
Robert Silasi‐Mansat United States 24 594 0.7× 364 1.1× 76 0.3× 61 0.4× 143 0.9× 53 1.9k
Jason D. Coombes United Kingdom 19 324 0.4× 135 0.4× 131 0.5× 87 0.5× 105 0.6× 31 1.1k
Clizia Chinello Italy 25 1.1k 1.4× 126 0.4× 120 0.4× 162 1.0× 44 0.3× 70 1.8k
Josef Ehling Germany 24 631 0.8× 198 0.6× 146 0.5× 1.2k 6.9× 67 0.4× 37 2.7k
Ewa Gorodkiewicz Poland 22 706 0.9× 153 0.5× 78 0.3× 315 1.9× 39 0.2× 95 1.3k
Yimin Lu China 19 548 0.7× 364 1.1× 48 0.2× 80 0.5× 48 0.3× 63 1.3k

Countries citing papers authored by Matthew J. Lazzara

Since Specialization
Citations

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

Fields of papers citing papers by Matthew J. Lazzara

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Matthew J. Lazzara. 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 Matthew J. Lazzara. The network helps show where Matthew J. Lazzara may publish in the future.

Co-authorship network of co-authors of Matthew J. Lazzara

This figure shows the co-authorship network connecting the top 25 collaborators of Matthew J. Lazzara. A scholar is included among the top collaborators of Matthew J. Lazzara 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 Matthew J. Lazzara. Matthew J. Lazzara 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
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Xiao, Aizhen, Qing Zhong, Pankaj Kumar, et al.. (2025). Synergistic activity of simvastatin and irinotecan chemotherapy against glioblastoma converges on TGF-β signaling. Journal of Neuro-Oncology. 174(3). 621–633. 1 indexed citations
3.
Myers, Paul J., Sara J. Adair, Jason R. Pitarresi, et al.. (2024). A Histone Methylation–MAPK Signaling Axis Drives Durable Epithelial–Mesenchymal Transition in Hypoxic Pancreatic Cancer. Cancer Research. 84(11). 1764–1780. 14 indexed citations
4.
Zhong, Qing, et al.. (2021). Data-Driven Computational Modeling Identifies Determinants of Glioblastoma Response to SHP2 Inhibition. Cancer Research. 81(8). 2056–2070. 7 indexed citations
5.
Thomas, Keena S., Paul J. Myers, Kristen A. Atkins, et al.. (2021). Breast Cancer Antiestrogen Resistance 3 (BCAR3) promotes tumor growth and progression in triple-negative breast cancer.. PubMed. 11(10). 4768–4787. 6 indexed citations
6.
Cornelison, Robert, et al.. (2021). CX-5461 Treatment Leads to Cytosolic DNA-Mediated STING Activation in Ovarian Cancer. Cancers. 13(20). 5056–5056. 18 indexed citations
7.
Campbell, Anne, et al.. (2020). ERK-dependent suicide gene therapy for selective targeting of RTK/RAS-driven cancers. Molecular Therapy. 29(4). 1585–1601. 4 indexed citations
8.
Walsh, Alice M., Gurpreet S. Kapoor, Janine M. Buonato, et al.. (2015). Sprouty2 Drives Drug Resistance and Proliferation in Glioblastoma. Molecular Cancer Research. 13(8). 1227–1237. 24 indexed citations
9.
Buonato, Janine M., Nicolas Skuli, Lijoy K. Mathew, et al.. (2014). Multivariate signaling regulation by SHP2 differentially controls proliferation and therapeutic response in glioma cells. Journal of Cell Science. 127(Pt 16). 3555–67. 42 indexed citations
10.
Lazzara, Matthew J., et al.. (2014). Disseminated cryptococcosis involving the head and neck. BMJ Case Reports. 2014. bcr2013202306–bcr2013202306. 3 indexed citations
11.
Buonato, Janine M. & Matthew J. Lazzara. (2013). ERK1/2 Blockade Prevents Epithelial–Mesenchymal Transition in Lung Cancer Cells and Promotes Their Sensitivity to EGFR Inhibition. Cancer Research. 74(1). 309–319. 135 indexed citations
12.
Rojas, Andrés, et al.. (2012). Diminished functional role and altered localization of SHP2 in non-small cell lung cancer cells with EGFR-activating mutations. Oncogene. 32(18). 2346–2355. 34 indexed citations
13.
Monast, Calixte S., et al.. (2012). Computational Analysis of the Regulation of EGFR by Protein Tyrosine Phosphatases. Biophysical Journal. 102(9). 2012–2021. 26 indexed citations
14.
15.
Lazzara, Matthew J. & Douglas A. Lauffenburger. (2008). Quantitative modeling perspectives on the ErbB system of cell regulatory processes. Experimental Cell Research. 315(4). 717–725. 22 indexed citations
16.
Lazzara, Matthew J. & William M. Deen. (2006). Model of albumin reabsorption in the proximal tubule. American Journal of Physiology-Renal Physiology. 292(1). F430–F439. 58 indexed citations
17.
Bolton, Glen, Austin W. Boesch, & Matthew J. Lazzara. (2006). The effects of flow rate on membrane capacity: Development and application of adsorptive membrane fouling models. Journal of Membrane Science. 279(1-2). 625–634. 70 indexed citations
18.
Deen, William M. & Matthew J. Lazzara. (2004). Glomerular filtration of albumin: How small is the sieving coefficient?. Kidney International. 66(92). S63–S64. 17 indexed citations
19.
Lazzara, Matthew J., et al.. (2004). Protein partitioning driven by excluded‐volume interactions in an aqueous nonionic micellar—gel system. Biotechnology and Bioengineering. 87(6). 695–703. 7 indexed citations
20.
Deen, William M., Matthew J. Lazzara, & Bryan D. Myers. (2001). Structural determinants of glomerular permeability. American Journal of Physiology-Renal Physiology. 281(4). F579–F596. 295 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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