Harnessing the predictive power of preclinical models for oncology drug development

Double-slit photoelectron interference in strong-field ionization of the neon dimer

Kunitski, M., Eicke, N., Huber, P., Köhler, J., Zeller, S., Voigtsberger, J., Schlott, N., Henrichs, K., Sann, H., Trinter, F., et al. (2019) · Institutional Repository of Leibniz Universität Hannover (Leibniz Universität Hannover) · doi:10.15488/5174 · Cited by 8,378

Double-slit photoelectron interference in strong-field ionization of the neon dimer

Kunitski, M., Eicke, N., Huber, P., Köhler, J., Zeller, S., Voigtsberger, J., Schlott, N., Henrichs, K., Sann, H., Trinter, F., et al. (2019) · Nature Communications · doi:10.1038/s41467-018-07882-8 · Cited by 8,162

Improved survival with vemurafenib in melanoma with BRAF V600E mutation

Chapman, P. B., Hauschild, A., Robert, C., Haanen, J. B., Ascierto, P., Larkin, J., Dummer, R., Garbe, C., Testori, A., Maio, M., et al. (2011) · New England Journal of Medicine · doi:10.1056/nejmoa1103782 · Cited by 7,720

Osimertinib in Untreated EGFR -Mutated Advanced Non–Small-Cell Lung Cancer

Soria, J.-C., Ohe, Y., Vansteenkiste, J., Reungwetwattana, T., Chewaskulyong, B., Lee, K. H., Dechaphunkul, A., Imamura, F., Nogami, N., Kurata, T., et al. (2018) · New England Journal of Medicine · doi:10.1056/nejmoa1713137 · Cited by 5,508

Cancer drug resistance: an evolving paradigm

Holohan, C., Van Schaeybroeck, S., Longley, D. B., & Johnston, P. G. (2013) · Nature Reviews Cancer · doi:10.1038/nrc3599 · Cited by 4,681

EGF receptor gene mutations are common in lung cancers from “never smokers” and are associated with sensitivity of tumors to gefitinib and erlotinib

Pao, W., Miller, V., Zakowski, M., Doherty, J., Politi, K., Sarkaria, I., Singh, B., Heelan, R., Rusch, V., Fulton, L., et al. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0405220101 · Cited by 4,338

Genomics of Drug Sensitivity in Cancer (GDSC): a resource for therapeutic biomarker discovery in cancer cells

Yang, W., Soares, J., Greninger, P., Edelman, E. J., Lightfoot, H., Forbes, S., Bindal, N., Beare, D., Smith, J. A., Thompson, I. R., et al. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks1111 · Cited by 4,051

Acquired Resistance of Lung Adenocarcinomas to Gefitinib or Erlotinib Is Associated with a Second Mutation in the EGFR Kinase Domain

Pao, W., Miller, V. A., Politi, K. A., Riely, G. J., Somwar, R., Zakowski, M. F., Kris, M. G., & Varmus, H. (2005) · PLoS Medicine · doi:10.1371/journal.pmed.0020073 · Cited by 3,527

Defining a Cancer Dependency Map

Tsherniak, A., Vazquez, F., Montgomery, P. G., Weir, B. A., Kryukov, G., Cowley, G. S., Gill, S., Harrington, W. F., Pantel, S., Krill-Burger, J. M., et al. (2017) · Cell · doi:10.1016/j.cell.2017.06.010 · Cited by 3,276

Inhibition of Hedgehog signaling enhances delivery of chemotherapy in a mouse model of pancreatic cancer

Olive, K. P., Jacobetz, M. A., Davidson, C. J., Gopinathan, A., McIntyre, D., Honess, D., Madhu, B., Goldgraben, M. A., Caldwell, M. E., Allard, D., et al. (2009) · Science · doi:10.1126/science.1171362 · Cited by 3,170

Organoids

Zhao, Z., Chen, X., Dowbaj, A. M., Sljukic, A., Bratlie, K., Lin, L., Fong, E. L. S., Balachander, G. M., Chen, Z., Soragni, A., et al. (2022) · Nature Reviews Methods Primers · doi:10.1038/s43586-022-00174-y · Cited by 1,082

Patient-derived xenograft models in cancer therapy: technologies and applications

Liu, Y., Wu, W., Cai, C., Zhang, H., Shen, H., & Han, Y. (2023) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-023-01419-2 · Cited by 533

Drug resistance in cancer: molecular mechanisms and emerging treatment strategies

Li, J., Hu, J., Yang, Y., Zhang, H., Liu, Y., Fang, Y., Qu, L., Lin, A., Luo, P., Jiang, A., et al. (2025) · Molecular Biomedicine · doi:10.1186/s43556-025-00352-w · Cited by 120

Coding, or non-coding, that is the question

Poliseno, L., Lanza, M., & Pandolfi, P. P. (2024) · Cell Research · doi:10.1038/s41422-024-00975-8 · Cited by 101

Personalized chordoma organoids for drug discovery studies

Al Shihabi, A., Davarifar, A., Nguyen, H. T. L., Tavanaie, N., Nelson, S. D., Yanagawa, J., Federman, N., Bernthal, N., Hornicek, F., & Soragni, A. (2022) · Science Advances · doi:10.1126/sciadv.abl3674 · Cited by 75

A multi-organ-on-chip to recapitulate the infiltration and the cytotoxic activity of circulating NK cells in 3D matrix-based tumor model

Marzagalli, M., Pelizzoni, G., Fedi, A., Vitale, C., Fontana, F., Bruno, S., Poggi, A. D., Dondero, A., Aiello, M., Castriconi, R., et al. (2022) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2022.945149 · Cited by 63

Recommendations for robust and reproducible preclinical research in personalised medicine

Fosse, V., Oldoni, E., Biétrix, F., Budillon, A., Daskalopoulos, E. P., Fratelli, M., Gerlach, B., Groenen, P. M. A., Hölter, S. M., Menon, J. M. L., et al. (2023) · BMC Medicine · doi:10.1186/s12916-022-02719-0 · Cited by 58

PharmaFormer predicts clinical drug responses through transfer learning guided by patient derived organoid

Zhou, Y., Dai, Q., Xu, Y., Wu, S., Cheng, M., & Zhao, B. T. (2025) · npj Precision Oncology · doi:10.1038/s41698-025-01082-6 · Cited by 50

Deep transfer learning for clinical decision-making based on high-throughput data: comprehensive survey with benchmark results

Toseef, M., Petinrin, O. O., Wang, F., Rahaman, S., Liu, Z., Li, X., & Wong, K. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad254 · Cited by 44

Serrated colorectal cancer: preclinical models and molecular pathways

Aiderus, A., Barker, N., & Tergaonkar, V. (2023) · Trends in cancer · doi:10.1016/j.trecan.2023.09.011 · Cited by 42

Resurgence of syphilis: focusing on emerging clinical strategies and preclinical models

Xiong, S., Liu, Z., Zhang, X., Huang, S., Ding, X., Zhou, J., Yao, J., Li, W., Liu, S., & Zhao, F. (2023) · Journal of Translational Medicine · doi:10.1186/s12967-023-04685-4 · Cited by 43

Predicting drug–target binding affinity with cross-scale graph contrastive learning

Wang, J., Xiao, Y., Shang, X., & Peng, J. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad516 · Cited by 39

OBSERVE: guidelines for the refinement of rodent cancer models

Vleeschauwer, S. I. D., van de Ven, M., Oudin, A., Debusschere, K., Connor, K., Byrne, A. T., Ram, D., Rhebergen, A. M., Raeves, Y. D., Dahlhoff, M., et al. (2024) · Nature Protocols · doi:10.1038/s41596-024-00998-w · Cited by 40

Computational estimation of quality and clinical relevance of cancer cell lines

Trastulla, L., Noorbakhsh, J., Vazquez, F., McFarland, J., & Iorio, F. (2022) · Molecular Systems Biology · doi:10.15252/msb.202211017 · Cited by 31

In vitro construction of lung cancer organoids by 3D bioprinting for drug evaluation

Dong, Q., Su, X., Li, X., Zhou, H., Jian, H., Bai, S., Yin, J., & You, Q. (2023) · Colloids and Surfaces A Physicochemical and Engineering Aspects · doi:10.1016/j.colsurfa.2023.131288 · Cited by 29

Next‐Generation Preclinical Functional Testing Models in Cancer Precision Medicine: CTC‐Derived Organoids

Huang, L., Xu, Y., Wang, N., Yi, K., Xi, X., Si, H., Zhang, Q., Xiang, M., Rong, Y., Yuan, Y., et al. (2023) · Small Methods · doi:10.1002/smtd.202301009 · Cited by 30

Early phase clinical trials in oncology: Realising the potential of seamless designs

Jaki, T., Burdon, A., Chen, X., Mozgunov, P., Zheng, H., & Baird, R. D. (2023) · European Journal of Cancer · doi:10.1016/j.ejca.2023.05.005 · Cited by 23

Exploring New Dimensions of Tumor Heterogeneity: The Application of Single Cell Analysis to Organoid‐Based 3D In Vitro Models

Landon‐Brace, N., Li, N. T., & McGuigan, A. P. (2023) · Advanced Healthcare Materials · doi:10.1002/adhm.202300903 · Cited by 25

Combination drug screen targeting glioblastoma core vulnerabilities reveals pharmacological synergisms

Ariey-Bonnet, J., Bergès, R., Montero, M.-P., Mouysset, B., Piris, P., Müller, K., Pinna, G., Failes, T. W., Arndt, G. M., Morando, P., et al. (2023) · EBioMedicine · doi:10.1016/j.ebiom.2023.104752 · Cited by 22

Salicylanilides and Their Anticancer Properties

Kauerová, T., Pérez‐Pérez, M., & Kollár, P. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24021728 · Cited by 23

Experimental evidence of quint points and non-quantum chirality in a minimalist autonomous electronic oscillator

VOLOS, C. K., & Gallas, J. A. C. (2022) · The European Physical Journal Plus · doi:10.1140/epjp/s13360-021-02318-7 · Cited by 20

Large inherent variability in data derived from highly standardised cell culture experiments

Reddin, I. G., Fenton, T. R., Wass, M. N., & Michaelis, M. (2023) · Pharmacological Research · doi:10.1016/j.phrs.2023.106671 · Cited by 21

Investigating the potential of oncolytic viruses for cancer treatment via MSC delivery

Ghaleh, H. E. G., Vakilzadeh, G., Zahiri, A., & Farzanehpour, M. (2023) · Cell Communication and Signaling · doi:10.1186/s12964-023-01232-y · Cited by 19

Transfer learning of clinical outcomes from preclinical molecular data, principles and perspectives

Kowald, A., Barrantes, I., Möller, S., Palmer, D. H., Escobar, H. M., Schwerk, A. M. K., & Fuellen, G. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac133 · Cited by 17

A Genetically Encoded Magnetic Resonance Imaging Reporter Enables Sensitive Detection and Tracking of Spontaneous Metastases in Deep Tissues

Nyström, N. N., McRae, S. W., Martínez, F. M., Kelly, J. J., Scholl, T. J., & Ronald, J. A. (2022) · Cancer Research · doi:10.1158/0008-5472.can-22-2770 · Cited by 16

The endocannabinoid system in cancer biology: a mini-review of mechanisms and therapeutic potential

Salum, K. C. R., Miranda, G. B. A., Dias, A. L., Carneiro, J. R. I., Bozza, P. T., da Fonseca, A. C. P., & Silva, T. (2025) · Oncology Reviews · doi:10.3389/or.2025.1573797 · Cited by 15

Capturing the third dimension in drug discovery: Spatially-resolved tools for interrogation of complex 3D cell models

Simão, D. F., Gomes, C. M., Alves, P. M., & Brito, C. (2021) · Biotechnology Advances · doi:10.1016/j.biotechadv.2021.107883 · Cited by 15

Homologous Recombination Deficiency Unrelated to Platinum and PARP Inhibitor Response in Cell Line Libraries

Takamatsu, S., Murakami, K., & Matsumura, N. (2024) · Scientific Data · doi:10.1038/s41597-024-03018-4 · Cited by 15

Ovarian Cancer Patient-Derived Organoid Models for Pre-Clinical Drug Testing

Fashemi, B., van Biljon, L., Rodriguez, J., Graham, O., Mullen, M., & Khabele, D. (2023) · Journal of Visualized Experiments · doi:10.3791/65068 · Cited by 15

The role of optimizers in developing data-driven model for predicting lake water quality incorporating advanced water quality model

Uddin, M. G., Bamal, A., Diganta, M. T. M., Sajib, A. M., Rahman, A., Abioui, M., & Olbert, A. I. (2025) · Alexandria Engineering Journal · doi:10.1016/j.aej.2025.03.022 · Cited by 14

Melanoma growth in non-chemically modified translucid bacterial nanocellulose hollow and compartimentalized spheres

Fucina, G., Cesca, K., Berti, F. V., Biavatti, M. W., & Porto, L. M. (2022) · Biochimica et Biophysica Acta (BBA) - General Subjects · doi:10.1016/j.bbagen.2022.130183 · Cited by 13

Development and Characterization of an In Vitro Round Window Membrane Model for Drug Permeability Evaluations

Singh, R., Birru, B., Veit, J. G. S., Arrigali, E. M., & Serban, M. A. (2022) · Pharmaceuticals · doi:10.3390/ph15091105 · Cited by 13

Establishment and large-scale validation of a three-dimensional tumor model on an array chip for anticancer drug evaluation

Xiao, R., Jin, L., Xie, N., Luo, P., Gao, W., Tu, P., & Ai, X. (2022) · Frontiers in Pharmacology · doi:10.3389/fphar.2022.1032975 · Cited by 13

Molecular and functional landscape of malignant serous effusions for precision oncology

Wegmann, R., Bankel, L., Festl, Y., Lau, K., Lee, S., Arnold, F. M., Cappelletti, V., Fehr, A., Picotti, P., Dedes, K. J., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-52694-8 · Cited by 14

Tyrosine Kinase Inhibitor Lenvatinib Based Nano Formulations and Cutting-Edge Scale-Up Technologies in revolutionizing Cancer Therapy

Shaikh, S., Chary, P. S., & Mehra, N. K. (2025) · ACS Applied Bio Materials · doi:10.1021/acsabm.4c01527 · Cited by 13

Emerging applications of stimulated Raman scattering microscopy for medicinal chemistry and drug discovery

Braddick, H. J., Tipping, W. J., Grant, E. K., Tomkinson, N. C. O., Faulds, K., & Graham, D. (2025) · Chemical Society Reviews · doi:10.1039/d5cs00748h · Cited by 11

A chronicle review of new techniques that facilitate the understanding and development of optimal individualized therapeutic strategies for chordoma

Zhao, C., Tan, T., Zhang, E., Wang, T., Gong, H., Jia, Q., Liu, T., Yang, X., Zhao, J., Wu, Z., et al. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2022.1029670 · Cited by 9

Neuroimaging findings in preclinical amyotrophic lateral sclerosis models—How well do they mimic the clinical phenotype? A systematic review

Cannon, A. E., Zürrer, W. E., Zejlon, C., Kulcsár, Z., Lewandowski, S. A., Piehl, F., Granberg, T., & Ineichen, B. V. (2023) · Frontiers in Veterinary Science · doi:10.3389/fvets.2023.1135282 · Cited by 11

Cancer models for reverse and forward translation

(2022) · Nature Cancer · doi:10.1038/s43018-022-00346-5 · Cited by 8

Patient-derived tumoroids and proteomic signatures: tools for early drug discovery

Lê, H., Deforges, J., Cutolo, P., Lamarque, A., Hua, G., Lindner, V., Jain, S., Balloul, J., Benkirane-Jessel, N., & Quéméneur, É. (2024) · Frontiers in Immunology · doi:10.3389/fimmu.2024.1379613 · Cited by 7

Leveraging Multimodal Foundation Models in Biliary Tract Cancer Research

Singh, Y., Andersen, J. B., Hathaway, Q. A., Vera-Garcia, D. V., Keishing, V., Venkatesh, S. K., Salehi, S., Povero, D., Wallace, M. B., Gores, G. J., et al. (2025) · Tomography · doi:10.3390/tomography11090096 · Cited by 7

Patient-derived models: Promising tools for accelerating the clinical translation of breast cancer research findings

Cao, C., Lu, X., Guo, X., Zhao, H., & Gao, Y. (2023) · Experimental Cell Research · doi:10.1016/j.yexcr.2023.113538 · Cited by 7

3D bioprinting of alginate for cancer therapy: A review

Rastin, H., & Abousalman‐Rezvani, Z. (2025) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2025.145494 · Cited by 6

Nanoformulated Phytochemicals Against Pancreatic Cancer: Emerging Advances in Therapeutic Strategies

Bravo-Vázquez, L. A., García, A. R., Maciel-Alemán, K., Rodríguez-González, G. Y., Sahare, P., Luna‐Bárcenas, G., Duttaroy, A. K., & Paul, S. (2026) · International Journal of Nanomedicine · doi:10.2147/ijn.s571767 · Cited by 6

Organoids, organ-on-a-chip, and microtumors: Biomimetic 3D tumor models advancing drug development and precision medicine

Xia, M., Wu, G., Wu, D., Hu, W., Deng, H., & Wang, S. (2026) · Acta Pharmaceutica Sinica B · doi:10.1016/j.apsb.2026.03.013 · Cited by 7

The preclinical gap in pancreatic cancer and radiotherapy

Tesson, M., & Morton, J. P. (2024) · Disease Models & Mechanisms · doi:10.1242/dmm.050703 · Cited by 6

Multiparametric Longitudinal Profiling of RCAS-tva-Induced PDGFB-Driven Experimental Glioma

Becker, H., Vega, S. C., Patzwaldt, K., Przystal, J. M., Walter, B., MICHELOTTI, F. C., Canjuga, D., Tatagiba, M. S., Pichler, B. J., Beck, S. C., et al. (2022) · Brain Sciences · doi:10.3390/brainsci12111426 · Cited by 7

Modeling Liver Development and Disease in a Dish

Iqbal, W., Wang, Y., Sun, P., & Zhou, X. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms242115921 · Cited by 6

Improved analysis of in vivo drug combination experiments with a comprehensive statistical framework and web-tool

Romero-Becerra, R., Zhao, Z. Q., Nebdal, D. J. H., Müller, E., Bergholtz, H., Norum, J. H., & Aittokallio, T. A. (2025) · Nature Communications · doi:10.1038/s41467-025-65218-9 · Cited by 7

Neuronal Organoid Engineering and Disease-Focused High-Throughput Neuropharmacology: Advances, Limitations, and Translational Strategies

Far, B. F., Omidvar, K., Heidari, E., Ebrahimi, M., Mohammadi, Y., & Ertaş, Y. N. (2025) · ACS Pharmacology & Translational Science · doi:10.1021/acsptsci.5c00407 · Cited by 5

Cardiac magnetic resonance imaging characterization of acute rejection in a porcine heterotopic heart transplantation model

Mendiola, M., Milano, C. A., Glass, C., Bowles, D. E., & Wendell, D. C. (2024) · PLoS ONE · doi:10.1371/journal.pone.0304588 · Cited by 5

Translational Relevance and Future Integration of the Oncopig Cancer Model in Preclinical Applications

Elkhadragy, L., Gaba, R. C., Niemeyer, M. M., Schook, L. B., & Schachtschneider, K. M. (2024) · Annual Review of Animal Biosciences · doi:10.1146/annurev-animal-111523-102125 · Cited by 5

Biologically relevant integration of transcriptomics profiles from cancer cell lines, patient-derived xenografts, and clinical tumors using deep learning

Dimitrieva, S., Janssens, R., Li, G., Szałata, A., Gopalakrishnan, R., Parmar, C., Kauffmann, A., & Durand, E. Y. (2025) · Science Advances · doi:10.1126/sciadv.adn5596 · Cited by 5

Using Patient-Derived Xenograft (PDX) Models as a ‘Black Box’ to Identify More Applicable Patients for ADP-Ribose Polymerase Inhibitor (PARPi) Treatment in Ovarian Cancer: Searching for Novel Molecular and Clinical Biomarkers and Performing a Prospective Preclinical Trial

Chen, J., Li, Y., Wang, H., Li, T., Gu, Y. L., Wang, W., Shan, Y., Yin, J., Wang, Y., Qin, M., et al. (2022) · Cancers · doi:10.3390/cancers14194649 · Cited by 5

Critical Comments by Food and Drug Administration Reviewers on Patient-Reported Outcomes in Food and Drug Administration Regulatory Submissions (2018-2021)

Slota, C., Norcross, L., Comerford, E., Sasané, M., Zheng, Y., & Gnanasakthy, A. (2024) · Value in Health · doi:10.1016/j.jval.2024.02.011 · Cited by 4

Comparative Proteomic Characterization of Serum-Free Cultivated HepG2 Cells Reveals Upregulated Drug Metabolism and Increased Oxidative Stress Protection

Pfeifer, L.-M., Singh, J., Zimmer, A., Krenkel, M., Fischer, F., Sensbach, J., Pipp, F. C., Werkmann, D., & Hewitt, P. G. (2025) · Journal of Proteome Research · doi:10.1021/acs.jproteome.5c00100 · Cited by 4

A porcine model of acute rejection for cardiac transplantation

Mendiola, M., Chiang, Y. P., Glass, C., Wendell, D. C., Swain-Lenz, D., Ho, S., Fudim, M., Lee, F. H., Kang, L., Smith, M. F., et al. (2025) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2025.1549377 · Cited by 4

OrgXenomics: an integrated proteomic knowledge base for patient-derived organoid and xenograft

Zhang, Y., Lian, X., Xu, H., Zhu, S., Zhang, H., Ni, Z., Fu, T., Liu, S., Tao, L., Zhou, Y., et al. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae861 · Cited by 4

Bridging the Gap: How Organ‐on‐a‐Chip Technology Facilitates the Battle against Glioma

Liu, S., Gong, Z., Zhou, D., Ayala‐Nunez, V., Xu, T., & Wick, P. (2025) · Small Science · doi:10.1002/smsc.202500154 · Cited by 5

A high-throughput 3D culture microfluidic platform for multi-parameter phenotypic and omics profiling of patient-derived organoidsPreprint

Botrugno, O. A., Bianchi, E., Bruno, J. M., Gallo, G. F. M., Felici, C., Sommella, E., Stefano, P. D., Giansanti, V., Rossella, V., Merciai, F., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.12.28.630600 · Cited by 4

State of Research on Tissue Engineering with 3D Printing for Breast Reconstruction

Velasquez, G. D. D. S., Tanas, Y., Taraballi, F., Herzog, T., & Spiegel, A. J. (2025) · Journal of Clinical Medicine · doi:10.3390/jcm14196737 · Cited by 4

Murine models of lung adenocarcinoma: Valuable tools for preclinical investigation

Shang, W., Lai, C., Luo, S., & Chen, L. (2025) · Gene · doi:10.1016/j.gene.2025.149802 · Cited by 3

Harnessing PDX and PDX 2.0: the next-generation paradigm for precision oncology and translational breakthroughs

Jian, C., Fu, H., Wu, W., Zhang, N., Liu, Z., Xia, Z., Luo, P., Zhang, H., & Cheng, Q. (2026) · Molecular Cancer · doi:10.1186/s12943-026-02594-w · Cited by 4

Unveiling immune cell response disparities in human primary cancer-associated fibroblasts between two- and three-dimensional cultures

Yang, J.-P., Kulkarni, N. N., Yamaji, M., Shiraishi, T., Pham, T. Q., Do, H., Aiello, N. M., Shaw, M. H., Nakamura, T., Abiru, A., et al. (2024) · PLoS ONE · doi:10.1371/journal.pone.0314227 · Cited by 3

Synergistic Effect of Lenvatinib and Chemotherapy in Hepatocellular Carcinoma Using Preclinical Models

Wang, M., Yao, X., Bo, Z., Zheng, J., Yu, H., Xie, X., Lin, Z., Wang, Y., Chen, G., & Wu, L. (2023) · Journal of Hepatocellular Carcinoma · doi:10.2147/jhc.s395474 · Cited by 3

Completion Rate and Positive Results Reporting Among Immunotherapy Trials in Breast Cancer, 2004-2023

Mariani, M., Viale, G., Galbardi, B., Licata, L., Bosi, C., Dugo, M., Notini, G., Naldini, M. M., Callari, M., Criscitiello, C., et al. (2024) · JAMA Network Open · doi:10.1001/jamanetworkopen.2024.23390 · Cited by 3

Advancing Frontiers in Colorectal Cancer: Exploring Novel Insights and Emerging Trends in Research and Therapy

Ali, A. A. (2024) · Advancements in Life Sciences · doi:10.62940/als.v11i4.3335 · Cited by 3

Patient-Derived Xenografts of Breast Cancer

Marangoni, E. (2025) · Advances in experimental medicine and biology · doi:10.1007/978-3-031-70875-6_7 · Cited by 3

Sensitive Spatiotemporal Tracking of Spontaneous Metastasis in Deep Tissues via a Genetically-Encoded Magnetic Resonance Imaging ReporterPreprint

Nyström, N. N., McRae, S. W., Martinez, F., Kelly, J. J., Scholl, T., & Ronald, J. (2022) · bioRxiv · doi:10.1101/2022.02.09.479610 · Cited by 2

Predicting PROTAC-targeted Degradation and Designing Androgen Receptor Degraders with AiPROTACPreprint

Zhang, L., Sun, R., Li, X., Ren, C., Long, Y., Tan, W., Wang, X., Zou, K., Yang, X., Wu, M., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.02.26.640266 · Cited by 5

Cathepsin C: a critical mediator between immune response and cardiovascular disease – therapeutic implications of enzyme inhibition

Aljabali, A. A. A., Obeid, M. A., Gammoh, O., Alshaer, W., Qnais, E. Y., Alqudah, A. M., Mishra, V. K., Mishra, Y., & El‐Tanani, M. (2025) · Drug Development and Industrial Pharmacy · doi:10.1080/03639045.2025.2571722 · Cited by 2

Advancing anticancer chelation therapy: Insights from enhancing cytotoxic potency, targeted cellular delivery, metal-based immunomodulation, and combinatorial strategies

Acosta, C., Claudio-Ares, O., Ullah, I., Cruz, L. C., Ducey, A. C., Vidal, A., Escalante-Castaneda, A., Maldonado-Santos, A. R., Liu, B., Villanueva, C. N., et al. (2025) · Inorganica Chimica Acta · doi:10.1016/j.ica.2025.123004 · Cited by 2

3D Tumor Models—Nanomedicine in the Third Dimension

Bravo, M. L., Zundert, I. V., Uji‐i, H., Fortuni, B., & Rocha, S. (2024) · Nanomedicine and nanotoxicology · doi:10.1007/978-981-97-5288-1_11 · Cited by 2

Experimental Modeling and Treatment Strategies for Peritoneal Carcinomatosis

Canda, A. E., & Sever, T. (2024) · Turkish Journal of Colorectal Disease · doi:10.4274/tjcd.galenos.2024.2024-10-5 · Cited by 2

BioXNet: a biologically inspired neural network for deciphering anti-cancer drug response in precision medicinePreprint

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