Florida Cancer Research Network
Florida Cancer Research Network
Florida Cancer Research Network

Publications

Full Reviewed Journal Articles

 

Cao B, Yu X, Gonzalez G, Murthy AR, Li T, Shen Y, Yao S, Wang X. CGPA: a multi-context cancer gene prognosis atlas. Mol Cancer Res. 2026 Jan 13. doi: 10.1158/1541-7786.MCR-24-1186. Epub ahead of print. PMID: 41528384.

DOI:  10.1158/1541-7786.MCR-24-1186.

Natarajan U, Rathinavelu A. Regulation of PD-L1 Expression by SAHA-Mediated Histone Deacetylase Inhibition in Lung Cancer Cells. Cancers (Basel). 2025;17(17):2919. Published 2025 Sep 5.

DOI:  doi:10.3390/cancers17172919

Martos, M. P.; Lee, M. S.; Merchant, N. B.; Kobetz, E.; Hester, C. A.; Stathias, V.; Maithel, S. K.; Schürer, S.; Datta, J. Neighborhood Disadvantage is Associated with Worse Pathologic Response in Pancreatic Cancer. Annals of Surgical Oncology 2025.

DOI: 10.1245/s10434-025-17024-2.

Fig. 1From: Neighborhood Disadvantage is Associated with Worse Pathologic Response in Pancreatic Cancer
Fig. 1 From: Neighborhood Disadvantage is Associated with Worse Pathologic Response in Pancreatic Cancer

Schulz, J. M., Schürer, S. I., Reynolds, R. C., & Schürer, S. C. (2025). PRosettaC outperforms AlphaFold3 for modeling PROTAC ternary complexes. Scientific reports, 15(1), 37620.

DOI:  https://doi.org/10.1038/s41598-025-21502-8

Hu, J.; Allen, B. K.; Stathias, V.; Ayad, N. G.; Schurer, S. C. Kinome-Wide Virtual Screening by Multi-Task Deep Learning. Int J Mol Sci 2024, 25 (5).

DOI: 10.3390/ijms25052538.

Figure 4. Effect of the number of active compounds model performance. Scatter plots ROC score data distribution for all 342 kinase tasks. Each box represents a specific model using either the Known Active–Known Inactive (KA–KI) or Known Active–Presumed Inactive (KA–PI) datasets.
Figure 4. Effect of the number of active compounds model performance. Scatter plots ROC score data distribution for all 342 kinase tasks. Each box represents a specific model using either the Known Active–Known Inactive (KA–KI) or Known Active–Presumed Inactive (KA–PI) datasets.

Full Article Preprints

Schulz, J. M.; Schürer, S. I.; Reynolds, R. C.; Schürer, S. C. Benchmarking the Builders: A Comparative Analysis of PRosettaC and AlphaFold3 for Predicting PROTAC Ternary Complexes. Research Square 2025, rs.3.rs-6866610;

DOI: 10.21203/rs.3.rs-6866610/v1.

Sobhan, M.; Islam, M.M.; Mondal, A.M. Interpreting Lung Cancer Health Disparity at Transcriptome Level. bioRxiv2025 2025;

DOI: 10.1101/2025.01.09.632292.

Ocasio, B. A.; Hu, J.; Stathias, V.; Martinez, M. J.; Burnstein, K. L.; Schurer, S. C. Pan-Cancer Drug Sensitivity Prediction from Gene Expression using Deep Learning. bioRxiv 2024.

DOI: 10.1101/2024.11.15.623715.

Abstracts

Sinclair M.; Obregon C.; Chung C.; Vidovic D.; Rupprecht L.; Pissinis J.; Pilarczyk M.; Sotolongo F.; Jagodnik K. M.; Krenz T.; Natarajan U.; Kobetz E.; Mondal A. M.; Cen L.; Wang X.; Rathinavelu A.; Chalk S.;Bian J.; Lee J. H.; Song Q.; Stathias V.; Schürer S. C. Abstract 1086: Introducing the Florida Cancer Research (FL CARES) Network and the Platform for Accelerating Collaborative Computational Cancer Research (PAC3R). Cancer Research 2025, 85(8_Supplement_1), 1086–1086;

DOI: 10.1158/1538-7445.AM2025-1086.

Pissinis J. I.; Mazariegos O.; Pilarczyk M.; Sinclair M. S.; Rupprecht L.; Chung C.; Sotolongo F.; Lichtenstein G.; Jagodnik K. M.; Figueredo J.; Seo P.; Obregon C. J.; Blicharz M.; Smol P.; Pyrkosz M.; Jura M.; Stathias V.; Schürer S. C. Abstract 1081: Sylvester Data Portal: Streamlined access to standardized clinicogenomic data. Cancer Research 2025, 85(8_Supplement_1), 1081–1081;

DOI: 10.1158/1538-7445.AM2025-1081.

Mendel O.; Sinclair M. S.; Jagodnik K. M.; Krenz T.; Pissinis J.; Stathias V.; Bartal A.; Schürer S. C. Abstract1052: Integrating geographical and socioeconomic data with genomic information for enhanced prediction of cancer risk using large language models and knowledge graphs. Cancer Research 2025, 85(8_Supplement_1), 1052–1052;

DOI: 10.1158/1538-7445.AM2025-1052.

Alssamani F. H.; Natarajan U.; Jaganathan S.; Rathinavelu A. Abstract 223: Evaluating the therapeutic potential of MDM2 inhibitor RG-7388 and epigenetic modulator CM-272 in cisplatin-resistant ovarian cancer. Cancer Research 2025, 85(8_Supplement_1), 223–223;

DOI: 10.1158/1538-7445.AM2025-223.

Tumati N.; Natarajan U.; Jaganathan S.; Rathinavelu A. Abstract 441: Evaluation of the ehects of MDM2 inhibitor and epigenetic modifiers in combination with AURKB inhibitors for treating lung cancer. Cancer Research 2025, 85(8_Supplement_1), 441–441;

DOI: 10.1158/1538-7445.AM2025-441.

Jagarlamudi A.; Tapia Stoll N.; Jaganathan S.; Natarajan U.; Rathinavelu A. Abstract 445: Epigenetic modification induced by DNMTs inhibitor enhances apoptosis through p21 pathway in neuroblastoma cells. Cancer Research 2025, 85(8_Supplement_1), 445–445;

DOI: 10.1158/1538-7445.AM2025-445.

Jaganathan S. S.; Natarajan U.; Rathinavelu A. Abstract 1618: Blocking MDM2 induces downregulation of PARP and enhances apoptosis in SK-N-SH neuroblastoma cells. Cancer
Research 2025, 85(8_Supplement_1), 1618–1618;

DOI: 10.1158/1538-7445.AM2025-1618.

Sobhan, M; Islam, M.M.; Mondal, A.M. “Interpreting Lung Cancer Health Disparity between African American Males and European American Males,” 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Lisbon, Portugal, 2024, pp. 7141-7143,

DOI: 10.1109/BIBM62325.2024.10822014.

Ocasio, B. A.; Hu, J.; Stathias, V.; Martinez, M. J.; Burnstein, K. L.; Schurer, S. C. 3O Pre-clinical pan-cancer drug repurposing via deep learning. ESMO Open 2024, 9.

DOI: 10.1016/j.esmoop.2024.103749.

Pescov, J. G.; Martinez, M. J.; Schurer, S. 82P Applying computational approaches to build a predictive protein structure and discover novel inhibitors for mitotic serine/threonine kinase BUB1B. ESMO Open 2024, 9.

DOI: 10.1016/j.esmoop.2024.103823.