Page 18 - GTM-4-2

Global Translational Medicine Game-changing drug response prediction

Acknowledgments doi: 10.1126/scitranslmed.aaw8412

None. 6. Idrisova KF, Simon HU, Gomzikova MO. Role of patient-
derived models of cancer in translational oncology. Cancers
Funding (Basel). 2022;15(1):139.

None. doi: 10.3390/cancers15010139
7. Bashor CJ, Hilton IB, Bandukwala H, Smith DM, Veiseh O.
Conflict of interest Engineering the next generation of cell-based therapeutics.
Nat Rev Drug Discov. 2022;21:655-675.
The authors declare no conflicts of interest.
doi: 10.1038/s41573-022-00476-6
Author contributions 8. Kim J, Koo BK, Knoblich JA. Human organoids: Model

Conceptualization: Chen Yeh systems for human biology and medicine. Nat Rev Mol Cell
Visualization: Shu-Ti Lin, Sharon Yeh Biol. 2020;21:571-584.
Writing–original draft: Chen Yeh, Andre Baranski doi: 10.1038/s41580-020-0259-3
Writing–review & editing: All authors 9. Yeh C. Enabling real-world data to accelerate the

Ethics approval and consent to participate development of innovative cancer biomarkers. Glob Med
Genet. 2023;10:97-100.
Not applicable. doi: 10.1055/s-0043-1768993

Consent for publication 10. Haslam A, Kim MS, Prasad V. Updated estimates of eligibility
for and response to genome-targeted oncology drugs among
Not applicable. US cancer patients, 2006-2020. Ann Oncol. 2021;32(7):
926-932.
Availability of data
doi: 10.1016/j.annonc.2021.04.003
Not applicable.
11. Yeh C, Lin ST, Lai HC. A transformative technology linking
References patient’s mRNA expression profile to anticancer drug
efficacy. Onco. 2024;4(3):143-162.
1. World Health Organization. Cancer Fact Sheet. World doi: 10.3390/onco4030012
Health Organization; 2022. Available from: https://www.
who.int/news-room/fact-sheets/detail/cancer [Last accessed 12. He D, Liu Q, Wu Y, Xie L. A context-aware deconfounding
on 2024 Aug 13]. autoencoder for robust prediction of personalized clinical
drug response from cell-line compound screening. Nat
2. Wong CH, Siah KW, Lo AW. Corrigendum: Estimation Mach Intell. 2022;4:879-892.
of clinical trial success rates and related parameters.
Biostatistics. 2018;20(2):366-366. doi: 10.1038/s42256-022-00541-0
doi: 10.1093/biostatistics/kxy072 13. Sagingalieva A, Kordzanganeh M, Kenbayev N, Kosichkina D,
Tomashuk T, Melnikov A. Hybrid quantum neural
3. Davis C, Naci H, Gurpinar E, Poplavska E, Pinto A, network for drug response prediction. Cancers (Basel).
Aggarwal A. Availability of evidence of benefits on overall 2023;15(10):2705.
survival and quality of life of cancer drugs approved by
European Medicines Agency: Retrospective cohort study of doi: 10.3390/cancers15102705
drug approvals 2009-13. BMJ. 2017;359:j4530. 14. Liu X, Zhang W. A subcomponent-guided deep learning
doi: 10.1136/bmj.j4530 method for interpretable cancer drug response prediction.
PLoS Comput Biol. 2023;19(8):e1011382.
4. Kim C, Prasad V. Cancer drugs approved on the basis of
a surrogate end point and subsequent overall survival: An doi: 10.1371/journal.pcbi.1011382
analysis of 5 years of US Food and Drug Administration 15. Yang Y, Li P. GPDRP: A multimodal framework for
Approvals. JAMA Intern Med. 2015;175(12):1992-1994. drug response prediction with graph transformer. BMC
doi: 10.1001/jamainternmed.2015.5868 Bioinformatics. 2023;24:484.
5. Lin A, Giuliano CJ, Palladino A, et al. Off-target toxicity is a doi: 10.1186/s12859-023-05618-0
common mechanism of action of cancer drugs undergoing 16. Taj F, Stein LD. MMDRP: Drug response prediction and
clinical trials. Sci Transl Med. 2019;11(509):eaaw8412. biomarker discovery using multi-modal deep learning.

Volume 4 Issue 2 (2025) 10 doi: 10.36922/gtm.5091