Massimo D’Apuzzo

6.5k total citations · 1 hit paper
73 papers, 3.3k citations indexed

About

Massimo D’Apuzzo is a scholar working on Oncology, Immunology and Genetics. According to data from OpenAlex, Massimo D’Apuzzo has authored 73 papers receiving a total of 3.3k indexed citations (citations by other indexed papers that have themselves been cited), including 29 papers in Oncology, 13 papers in Immunology and 12 papers in Genetics. Recurrent topics in Massimo D’Apuzzo's work include CAR-T cell therapy research (15 papers), Glioma Diagnosis and Treatment (12 papers) and Immune Cell Function and Interaction (7 papers). Massimo D’Apuzzo is often cited by papers focused on CAR-T cell therapy research (15 papers), Glioma Diagnosis and Treatment (12 papers) and Immune Cell Function and Interaction (7 papers). Massimo D’Apuzzo collaborates with scholars based in United States, Italy and Switzerland. Massimo D’Apuzzo's co-authors include Behnam Badie, Marco Baggiolini, Marcel Loetscher, Christine E. Brown, Stephen J. Forman, Michael E. Barish, Leopoldo Angrisani, Renate Starr, Michael C. Jensen and Pasquale Daponte and has published in prestigious journals such as Journal of Clinical Oncology, SHILAP Revista de lepidopterología and PLoS ONE.

In The Last Decade

Massimo D’Apuzzo

70 papers receiving 3.2k 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.

Bioactivity and Safety of IL13Rα2-Redirected Chimeric Antigen Receptor CD8+ T Cells in Patients with Recurrent Glioblastoma

2015 592 citations Christine E. Brown, Behnam Badie et al. Clinical Cancer Research profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Massimo D’Apuzzo United States	23	1.8k	1.2k	743	608	564	73	3.3k
Genevieve M. Boland United States	30	2.3k 1.3×	1.4k 1.2×	2.1k 2.9×	413 0.7×	676 1.2×	121	5.6k
Roel A. de Weger Netherlands	37	980 0.5×	1.1k 0.9×	1.4k 1.8×	397 0.7×	318 0.6×	158	4.5k
Cornelia Mauch Germany	49	3.0k 1.7×	1.4k 1.1×	2.7k 3.6×	331 0.5×	298 0.5×	208	7.9k
Camille Laurent France	39	1.9k 1.0×	980 0.8×	1.4k 1.9×	541 0.9×	358 0.6×	283	6.5k
Gary L. Gallia United States	47	1.1k 0.6×	269 0.2×	1.8k 2.4×	507 0.8×	1.4k 2.5×	235	6.9k
Simon Turcotte Canada	24	2.8k 1.5×	2.3k 1.9×	789 1.1×	431 0.7×	74 0.1×	72	4.8k
Dongguang Li China	21	1.1k 0.6×	1.1k 0.9×	498 0.7×	153 0.3×	168 0.3×	96	2.8k
Takao Sakai Japan	37	688 0.4×	582 0.5×	2.0k 2.7×	261 0.4×	152 0.3×	176	5.0k
Yong Beom Cho South Korea	41	3.2k 1.8×	255 0.2×	881 1.2×	263 0.4×	235 0.4×	288	5.7k
Andrea Boni Italy	24	2.2k 1.2×	1.9k 1.6×	1.6k 2.2×	382 0.6×	65 0.1×	54	4.3k

Countries citing papers authored by Massimo D’Apuzzo

Since Specialization

Citations

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

Fields of papers citing papers by Massimo D’Apuzzo

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Massimo D’Apuzzo. 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 Massimo D’Apuzzo. The network helps show where Massimo D’Apuzzo may publish in the future.

Co-authorship network of co-authors of Massimo D’Apuzzo

This figure shows the co-authorship network connecting the top 25 collaborators of Massimo D’Apuzzo. A scholar is included among the top collaborators of Massimo D’Apuzzo 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 Massimo D’Apuzzo. Massimo D’Apuzzo 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

Park, Byullee, Rui Cao, Yilin Luo, et al.. (2025). Rapid cancer diagnosis using deep learning–powered label-free subcellular-resolution photoacoustic histology. Science Advances. 11(47). eadz1820–eadz1820.

Barish, Michael E., Maryam Aftabizadeh, M. Suzette Blanchard, et al.. (2025). Chlorotoxin-directed CAR T cell therapy for recurrent glioblastoma: Interim clinical experience demonstrating feasibility and safety. Cell Reports Medicine. 6(8). 102302–102302. 2 indexed citations

Ye, Jian, Weihua Guo, Chongkai Wang, et al.. (2023). Peritumoral Immune-suppressive Mechanisms Impede Intratumoral Lymphocyte Infiltration into Colorectal Cancer Liver versus Lung Metastases. Cancer Research Communications. 3(10). 2082–2095. 6 indexed citations

Barish, Michael E., Lihong Weng, Yubo Zhai, et al.. (2022). Spatial organization of heterogeneous immunotherapy target antigen expression in high-grade glioma. Neoplasia. 30. 100801–100801. 11 indexed citations

Badie, Behnam, Michael E. Barish, Ammar Chaudhry, et al.. (2021). A phase 1 study to evaluate chimeric antigen receptor (CAR) T cells incorporating a chlorotoxin tumor-targeting domain for patients with MMP2+ Recurrent or progressive glioblastoma (NCT04214392).. Journal of Clinical Oncology. 39(15_suppl). TPS2662–TPS2662. 9 indexed citations

Chao, Joseph, Ting-Fang He, Massimo D’Apuzzo, et al.. (2021). A Phase 2 Trial Combining Pembrolizumab and Palliative Radiation Therapy in Gastroesophageal Cancer to Augment Abscopal Immune Responses. Advances in Radiation Oncology. 7(1). 100807–100807. 7 indexed citations

Portnow, Jana, Behnam Badie, M. Suzette Blanchard, et al.. (2020). Feasibility of intracerebrally administering multiple doses of genetically modified neural stem cells to locally produce chemotherapy in glioma patients. Cancer Gene Therapy. 28(3-4). 294–306. 15 indexed citations

Sahoo, Prativa, Paul Frankel, Julie A. Ressler, et al.. (2018). Early Changes in Tumor Perfusion from T1-Weighted Dynamic Contrast-Enhanced MRI following Neural Stem Cell-Mediated Therapy of Recurrent High-Grade Glioma Correlate with Overall Survival. Stem Cells International. 2018. 1–9. 5 indexed citations

Brown, Christine E., Brenda Aguilar, Renate Starr, et al.. (2017). Optimization of IL13Rα2-Targeted Chimeric Antigen Receptor T Cells for Improved Anti-tumor Efficacy against Glioblastoma. Molecular Therapy. 26(1). 31–44. 226 indexed citations

10.

Portnow, Jana, Timothy W. Synold, Behnam Badie, et al.. (2016). Neural Stem Cell–Based Anticancer Gene Therapy: A First-in-Human Study in Recurrent High-Grade Glioma Patients. Clinical Cancer Research. 23(12). 2951–2960. 127 indexed citations

11.

Shackleford, Gregory M., Kimberly Swanson, Ignacio González-Gómez, et al.. (2016). BarTeL, a Genetically Versatile, Bioluminescent and Granule Neuron Precursor-Targeted Mouse Model for Medulloblastoma. PLoS ONE. 11(6). e0156907–e0156907. 4 indexed citations

12.

Manuel, Edwin R., Massimo D’Apuzzo, Teodora Kaltcheva, et al.. (2015). Salmonella -Based Therapy Targeting Indoleamine 2,3-Dioxygenase Coupled with Enzymatic Depletion of Tumor Hyaluronan Induces Complete Regression of Aggressive Pancreatic Tumors. Cancer Immunology Research. 3(9). 1096–1107. 53 indexed citations

13.

Brown, Christine E., Behnam Badie, Michael E. Barish, et al.. (2015). Bioactivity and Safety of IL13Rα2-Redirected Chimeric Antigen Receptor CD8+ T Cells in Patients with Recurrent Glioblastoma. Clinical Cancer Research. 21(18). 4062–4072. 592 indexed citations breakdown →

14.

Hossain, Dewan Md Sakib, Sumanta K. Pal, Dayson Moreira, et al.. (2015). TLR9-Targeted STAT3 Silencing Abrogates Immunosuppressive Activity of Myeloid-Derived Suppressor Cells from Prostate Cancer Patients. Clinical Cancer Research. 21(16). 3771–3782. 156 indexed citations

15.

Brown, Christine E., Renate Starr, Brenda Aguilar, et al.. (2012). Stem-like Tumor-Initiating Cells Isolated from IL13Rα2 Expressing Gliomas Are Targeted and Killed by IL13-Zetakine–Redirected T Cells. Clinical Cancer Research. 18(8). 2199–2209. 185 indexed citations

16.

Brown, Christine E., Renate Starr, Catalina Martínez, et al.. (2009). Recognition and Killing of Brain Tumor Stem-Like Initiating Cells by CD8+ Cytolytic T Cells. Cancer Research. 69(23). 8886–8893. 113 indexed citations

17.

Kirley, Sandra D., Massimo D’Apuzzo, Gregory Y. Lauwers, et al.. (2005). The cables gene on chromosome 18Q regulates colon cancer progression in vivo. Cancer Biology & Therapy. 4(8). 861–863. 17 indexed citations

18.

Loetscher, Marcel, Ali Amara, Estelle Oberlin, et al.. (1997). TYMSTR, a putative chemokine receptor selectively expressed in activated T cells, exhibits HIV-1 coreceptor function. Current Biology. 7(9). 652–660. 76 indexed citations

19.

D’Apuzzo, Massimo, Antonius Rolink, Marcel Loetscher, et al.. (1997). The chemokine SDF‐1, stromal cell‐derived factor 1, attracts early stage B cell precursors via the chemokine receptor CXCR4. European Journal of Immunology. 27(7). 1788–1793. 279 indexed citations

20.

Uguccioni, Mariagrazia, Massimo D’Apuzzo, Marcel Loetscher, Béatrice Dewald, & Marco Baggiolini. (1995). Actions of the chemotactic cytokines MCP‐1, MCP‐2, MCP‐3, RANTES, MIP‐1α and MIP‐1β on human monocytes. European Journal of Immunology. 25(1). 64–68. 308 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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