AccScience Publishing / TD / Online First / DOI: 10.36922/TD025270059
Submit to TD
Cite this article
4
Download
93
Views
Journal Browser
Volume | Year
Issue
Search
Forthcoming Issue
Current Issue
View All
News and Announcements
View All
MINI-REVIEW

Seeing the whole elephant: From fragmented findings to integrative oncology

Licun Wu1,2*
Show Less
1 Latner Thoracic Surgery Research Laboratories, Division of Thoracic Surgery, Toronto General Hospital, Toronto General Hospital Research Institute, University Health Network, University of Toronto, Toronto, ON, Canada
2 Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
Tumor Discovery, 025270059 https://doi.org/10.36922/TD025270059
Received: 30 June 2025 | Revised: 11 October 2025 | Accepted: 7 November 2025 | Published online: 21 November 2025
© 2025 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
Download PDF
XML
Cite
Abstract

Cancer research has long faced the “Blind Men and the Elephant” dilemma, a metaphor derived from an ancient Indian parable. In the story, several blind men each touch a different part of an elephant—its trunk, leg, ear, or tail—and conclude that the creature resembles a snake, tree, fan, or rope. Each perception is valid within its narrow scope but incomplete when viewed in isolation. Similarly, cancer research has often been fragmented, with investigators focusing on isolated molecular pathways, cellular behaviors, or clinical outcomes without fully connecting these pieces into a unified picture of the disease. This systematic review explores how integrative and multidisciplinary approaches are overcoming that fragmentation to reshape our understanding of cancer. Drawing on advances in systems biology, multi-omics technologies, artificial intelligence, and collaborative research frameworks, we illustrate how convergence across disciplines enables a more comprehensive view of tumor biology and therapeutic response. Major initiatives such as The Cancer Genome Atlas, pan-cancer analyses, and emerging computational platforms exemplify how data integration can reveal patterns invisible to single-dimension studies. By highlighting both the transformative potential and persistent challenges of such integration—ranging from data harmonization to interdisciplinary communication—we propose a roadmap toward a holistic, collaborative, and patient-centered paradigm in oncology. In doing so, we aim to move beyond the limitations of partial understanding toward a collective vision that more accurately reflects the complexity of cancer.

Keywords
Cancer research
Multi-omics
Interdisciplinary integration
Pan-cancer analysis
Computational oncology
Translational medicine
Funding
None.
Conflict of interest
The author declares no conflict of interest.
References
  1. Sung H, Ferlay J, Siegel RL, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71(3):209-249. doi: 10.3322/caac.21660

 

  1. Marusyk A, Almendro V, Polyak K. Intra-tumour heterogeneity: A looking glass for cancer? Nat Rev Cancer. 2012;12(5):323-334. doi: 10.1038/nrc3261

 

  1. Holohan C, Van Schaeybroeck S, Longley DB, Johnston PG. Cancer drug resistance: An evolving paradigm. Nat Rev Cancer. 2013;13(10):714-726. doi: 10.1038/nrc3599

 

  1. Quail DF, Joyce JA. Microenvironmental regulation of tumor progression and metastasis. Nat Med. 2013;19(11): 1423-1437. doi: 10.1038/nm.3394

 

  1. Kitano H. Cancer as a robust system: Implications for anticancer therapy. Nat Rev Cancer. 2004;4(3):227-235. doi: 10.1038/nrc1293

 

  1. Hasin Y, Seldin M, Lusis A. Multi-omics approaches to disease. Genome Biol. 2017;18(1):83. doi: 10.1186/s13059-017-1215-1

 

  1. Esteva A, Robicquet A, Ramsundar B, et al. A guide to deep learning in healthcare. Nat Med. 2019;25(1):24-29. doi: 10.1038/s41591-018-0316-z

 

  1. Zhang Y, Wang P, Li X, et al. GABC: A comprehensive resource and Genome Atlas for Breast Cancer. Int J Cancer. 2021;148(4):988-994. doi: 10.1002/ijc.33347

 

  1. ICGC/TCGA Pan-Cancer Analysis of Whole Genomes Consortium. Pan-cancer analysis of whole genomes. Nature. 2020;578(7793):82-93. doi: 10.1038/s41586-020-1969-6

 

  1. Rozenblatt-Rosen O, Regev A, Oberdoerffer P, et al. The human tumor atlas network: Charting tumor transitions across space and time at single-cell resolution. Cell. 2020;181(2):236-249. doi: 10.1016/j.cell.2020.03.053

 

  1. Hanahan D, Weinberg RA. Hallmarks of cancer: The next generation. Cell. 2011;144(5):646-674. doi: 10.1016/j.cell.2011.02.013

 

  1. Junttila MR, de Sauvage FJ. Influence of tumour micro-environment heterogeneity on therapeutic response. Nature. 2013;501(7467):346-354. doi: 10.1038/nature12626

 

  1. Al-Lazikani B, Banerji U, Workman P. Combinatorial drug therapy for cancer in the post-genomic era. Nat Biotechnol. 2012;30(7):679-692. doi: 10.1038/nbt.2284

 

  1. Barabási AL, Gulbahce N, Loscalzo J. Network medicine: A network-based approach to human disease. Nat Rev Genet. 2011;12(1):56-68. doi: 10.1038/nrg2918

 

  1. Kitano H. Systems biology: A brief overview. Science. 2002;295(5560):1662-1664. doi: 10.1126/science.1069492

 

  1. Ideker T, Krogan NJ. Differential network biology. Mol Syst Biol. 2012;8:565. doi: 10.1038/msb.2011.99

 

  1. Saez-Rodriguez J, MacNamara A, Cook S. Modeling signaling networks to advance new cancer therapies. Annu Rev Biomed Eng. 2015;17:143-163. doi: 10.1146/annurev-bioeng-071813-104927

 

  1. Chen L, Liu R, Liu ZP, et al. Detecting early-warning signals for sudden deterioration of complex diseases by dynamical network biomarkers. Sci Rep. 2012;2:342. doi: 10.1038/srep00342

 

  1. Gavish A, Tyler M, Greenwald AC, et al. Hallmarks of transcriptional intratumour heterogeneity across a thousand tumours. Nature. 2023;618(7965):598-606. doi: 10.1038/s41586-023-06130-4

 

  1. Cieslik M, Chinnaiyan AM. Global genomics project unravels cancer’s complexity at unprecedented scale. Nature. 2020;578(7793):39-40. doi: 10.1038/d41586-020-00213-2

 

  1. Hoadley KA, Yau C, Hinoue T, et al. Cell-of-origin patterns dominate the molecular classification of 10,000 tumors from 33 types. Cell. 2018;173(2):291-304.e6. doi: 10.1016/j.cell.2018.03.022

 

  1. Sanchez-Vega F, Mina M, Armenia J, et al. Oncogenic signaling pathways in The Cancer Genome Atlas. Cell. 2018;173(2):321-337.e10. doi: 10.1016/j.cell.2018.03.035

 

  1. Guinney J, Dienstmann R, Wang X, et al. The consensus molecular subtypes of colorectal cancer. Nat Med. 2015;21(11):1350-1356. doi: 10.1038/nm.3967

 

  1. Marcus L, Donoghue M, Aungst S, et al. FDA approval summary: Entrectinib for the treatment of NTRK gene fusion solid tumors. Clin Cancer Res. 2021;27(4):928-932. doi: 10.1158/1078-0432.CCR-20-2771

 

  1. Shalem O, Sanjana NE, Zhang F. High-throughput functional genomics using CRISPR-Cas9. Nat Rev Genet. 2015;16(5):299-311. doi: 10.1038/nrg3899

 

  1. Hoffman GR, Rahal R, Buxton F, et al. Functional epigenetics approach identifies BRM/SMARCA2 as a critical synthetic lethal target in BRG1-deficient cancers. Proc Natl Acad Sci U S A. 2014;111(8):3128-3133. doi: 10.1073/pnas.1316793111

 

  1. Topol EJ. High-performance medicine: The convergence of human and artificial intelligence. Nat Med. 2019;25(1):44-56. doi: 10.1038/s41591-018-0300-7

 

  1. Kather JN, Heij LR, Grabsch HI, et al. Pan-cancer image-based detection of clinically actionable genetic alterations. Nat Cancer. 2020;1(8):789-799. doi: 10.1038/s43018-020-0087-6

 

  1. Lu MY, Chen TY, Williamson DFK, et al. AI-based pathology predicts origins for cancers of unknown primary. Nature. 2021;594(7861):106-110. doi: 10.1038/s41586-021-03512-4

 

  1. Jiang P, Gu S, Pan D, et al. Signatures of T cell dysfunction and exclusion predict cancer immunotherapy response. Nat Med. 2018;24(10):1550-1558. doi: 10.1038/s41591-018-0136-1

 

  1. Mascheroni P, Savvopoulos S, Alfonso JCL, Meyer- Hermann M, Hatzikirou H. Improving personalized tumor growth predictions using a Bayesian combination of mechanistic modeling and machine learning. Commun Med (Lond). 2021;1:19. doi: 10.1038/s43856-021-00020-4

 

  1. Wang Z, Butner JD, Kerketta R, Cristini V, Deisboeck TS. Simulating cancer growth with multiscale agent-based modeling. Semin Cancer Biol. 2015;30:70-78. doi: 10.1016/j.semcancer.2014.04.001

 

  1. Zhavoronkov A, Ivanenkov YA, Aliper A, et al. Deep learning enables rapid identification of potent DDR1 kinase inhibitors. Nat Biotechnol. 2019;37(9):1038-1040. doi: 10.1038/s41587-019-0224-x

 

  1. Selby P, Popescu R, Lawler M, Butcher H, Costa A. The value and future developments of multidisciplinary team cancer care. Am Soc Clin Oncol Educ Book. 2019;39:332-340. doi: 10.1200/EDBK_236857

 

  1. Curigliano G, Banerjee S, Cervantes A, et al. Managing cancer patients during the COVID-19 pandemic: An ESMO multidisciplinary expert consensus. Ann Oncol. 2020;31(10):1320-1335. doi: 10.1016/j.annonc.2020.07.010

 

  1. Sahu A, Oh Y, Peterson G, et al. In vivo optical imaging-guided targeted sampling for precise diagnosis and molecular pathology. Sci Rep. 2021;11:23124. doi: 10.1038/s41598-021-01447-4

 

  1. Boehm JS, Jacks T. Radical collaboration: Reimagining cancer team science. Cancer Discov. 2024;14(4):563-568. doi: 10.1158/2159-8290.CD-23-1496

 

  1. June CH, Sadelain M. Chimeric antigen receptor therapy. N Engl J Med. 2018;379(1):64-73. doi: 10.1056/NEJMra1706169

 

  1. Peng Y, Lee E. Microphysiological systems for cancer immunotherapy research and development. Adv Biol (Weinh). 2024;8(8):e2300077. doi: 10.1002/adbi.202300077

 

  1. Miotto R, Wang F, Wang S, Jiang X, Dudley JT. Deep learning for healthcare: Review, opportunities and challenges. Brief Bioinform. 2018;19(6):1236-1246. doi: 10.1093/bib/bbx044

 

  1. Grossman RL. Data lakes, clouds, and commons: A review of platforms for analyzing and sharing genomic data. Trends Genet. 2019;35(3):223-234. doi: 10.1016/j.tig.2018.12.006

 

  1. Sansone SA, Rocca-Serra P, Field D, et al. Toward interoperable bioscience data. Nat Genet. 2012;44(2):121-126. doi: 10.1038/ng.1054

 

  1. Knoppers BM, Thorogood AM. The Human Genome Organisation: Towards next-generation ethics. Genome Med. 2013;5:38. doi: 10.1186/gm442

 

  1. Urquhart R, Grunfeld E, Jackson L, Sargeant J, Porter GA. Cross-disciplinary research in cancer: An opportunity to narrow the knowledge-practice gap. Curr Oncol. 2013;20(6):e512-e521. doi: 10.3747/co.20.1487

 

  1. Chen IY, Pierson E, Rose S, Joshi S, Ferryman K, Ghassemi M. Ethical machine learning in healthcare. Annu Rev Biomed Data Sci. 2021;4:123-144. doi:10.1146/annurev-biodatasci-092820-114757

 

  1. Wilkinson MD, Dumontier M, Aalbersberg IJ, et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci Data. 2016;3:160018. doi: 10.1038/sdata.2016.18

 

  1. Hall KL, Stipelman BA, Vogel AL, et al. A four-phase model of transdisciplinary team-based research: Goals, team process and strategies. Am J Prev Med. 2012;42(2):159-169.

 

  1. Subramanian I, Verma S, Kumar S, Jere A, Anamika K. Multi-omics data integration, interpretation, and its application. Bioinform Biol Insights. 2020;14:1177932219899051. doi: 10.1177/1177932219899051

 

  1. Mertins P, Mani DR, Ruggles KV, et al. Proteogenomics connects somatic mutations to signalling in breast cancer. Nature. 2016;534(7605):55-62. doi: 10.1038/nature18003

 

  1. Zhang B, Wang J, Wang X, et al. Proteogenomic characterization of human colon and rectal cancer. Nature. 2014;513(7518):382-387. doi: 10.1038/nature13438

 

  1. Akbani R, Ng PKS, Werner HMJ, et al. A pan-cancer proteomic perspective on The Cancer Genome Atlas. Nat Commun. 2014;5:3887. doi: 10.1038/ncomms4887

 

  1. Huang KL, Mashl RJ, Wu Y, et al. Pathogenic germline variants in 10,389 adult cancers. Cell. 2018;173(2):355-370.e14. doi: 10.1016/j.cell.2018.03.039

 

  1. Du W, Elemento O. Cancer systems biology: Embracing complexity to develop better anticancer therapeutic strategies. Oncogene. 2015;34(25):3215-3225. doi: 10.1038/onc.2014.291

 

  1. Wei L, Niraula D, Gates EDH, et al. Artificial intelligence (AI) and machine learning (ML) in precision oncology: A review on enhancing discoverability through multiomics integration. Br J Radiol. 2023;96(1150):20230211. doi: 10.1259/bjr.20230211

 

  1. Grossman RL, Heath AP, Ferretti V, et al. Toward a shared vision for cancer genomic data. N Engl J Med. 2016;375(12):1109-1112 doi: 10.1056/NEJMp1607591

 

  1. Hanahan D. Hallmarks of cancer: New dimensions. Cancer Discov. 2022;12(1):31-46. doi: 10.1158/2159-8290.CD-21-1059

 

  1. Yankeelov TE, An G, Saut O, et al. Multi-scale modeling in clinical oncology: Opportunities and barriers to success. Ann Biomed Eng. 2016;44(9):2626-2641. doi: 10.1007/s10439-016-1691-6

 

  1. Huang S. Reconciling non-genetic plasticity with somatic evolution in cancer. Trends Cancer. 2021;7(4):309-322. doi: 10.1016/j.trecan.2020.12.007

 

  1. Alix-Panabières C, Pantel K. Liquid biopsy: From discovery to clinical application. Cancer Discov. 2021;11(4):858-873. doi: 10.1158/2159-8290.CD-20-1311

 

  1. Vis DJ, Lewin J, Liao RG, et al. Towards a global cancer knowledge network: Dissecting the current international cancer genomic sequencing landscape. Ann Oncol. 2017;28(5):1145-1151. doi: 10.1093/annonc/mdx037

 

  1. Shriver SP, Adams D, McKelvey BA, et al. Overcoming barriers to discovery and implementation of equitable pharmacogenomic testing in oncology. J Clin Oncol. 2024;42(10):1181-1192. doi: 10.1200/JCO.23.01748

 

  1. Cruz Rivera S, Aiyegbusi OL, Ives J, et al. Ethical considerations for the inclusion of patient-reported outcomes in clinical research: The PRO Ethics Guidelines. JAMA. 2022;327(19):1910-1919. doi: 10.1001/jama.2022.6421

 

  1. Macklin P. Key challenges facing data-driven multicellular systems biology. J Physiol. 2017;595(9):2897-2904. doi: 10.1113/JP273037

 

  1. Hutter C, Zenklusen JC. The cancer genome atlas: Creating lasting value beyond its data. Cell. 2018;173(2):283-285. doi: 10.1016/j.cell.2018.03.042

 

  1. Wing C, Simon K, Bello-Gomez RA. Designing difference in difference studies: Best practices for public health policy research. Annu Rev Public Health. 2018;39:453-469. doi: 10.1146/annurev-publhealth-040617-014507

 

  1. Azoulay L. Rationale, Strengths, and limitations of real-world evidence in oncology: A Canadian review and perspective. Oncologist. 2022;27(9):e731-e738. doi: 10.1093/oncolo/oyac114

 

  1. Errington TM, Denis A, Perfito N, Iorns E, Nosek BA. Challenges for assessing replicability in preclinical cancer biology. eLife. 2021;10:e67995. doi: 10.7554/eLife.67995

 

  1. Vogel AL, Knebel AR, Faupel-Badger JM, Portilla LM, Simeonov A. A systems approach to enable effective team science from the internal research program of the National Center for Advancing Translational Sciences. J Clin Transl Sci. 2021;5(1):e163. doi: 10.1017/cts.2021.811

 

  1. Hall KL, Vogel AL, Stipelman BA, et al. The science of team science: A review of the empirical evidence and research gaps on collaboration in science. Am Psychol. 2018;73(4):532-548. doi: 10.1037/amp0000319

 

  1. Barkley D, Moncada R, Pour M, et al. Cancer cell states recur across tumor types and form specific interactions with the tumor microenvironment. Nat Genet. 2022;54:1192-1201. doi: 10.1038/s41588-022-01141-9

 

  1. Jordan MI, Mitchell TM. Machine learning: Trends, perspectives, and prospects. Science. 2015;349(6245):255-260. doi: 10.1126/science.aaa8415

 

  1. Doria-Rose VP, Greenlee RT, Buist DSM, et al. Collaborating on data, science, and infrastructure: The 20-year journey of the cancer research network. EGEMS (Wash DC). 2019;7(1):7. doi: 10.5334/egems.273

 

  1. Jensen MA, Ferretti V, Grossman RL, Staudt LM. The NCI Genomic Data Commons as an engine for precision medicine. Blood. 2017;130(4):453-459. doi: 10.1182/blood-2017-03-735654
Next article in this issue
Share
Back to top
Tumor Discovery, Electronic ISSN: 2810-9775 Print ISSN: 3060-8597, Published by AccScience Publishing
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here
Accept