Oops, you're using an old version of your browser so some of the features on this page may not be displaying properly.

MINIMAL Requirements: Google Chrome 24+Mozilla Firefox 20+Internet Explorer 11Opera 15–18Apple Safari 7SeaMonkey 2.15-2.23

Poster session 03

1060P - Comparative investigation of neoadjuvant immunotherapy versus adjuvant immunotherapy in perioperative patients with cancer: A metrology informatics analysis based on machine learning

Date

14 Sep 2024

Session

Poster session 03

Topics

Clinical Research;  Tumour Immunology;  Cancer Intelligence (eHealth, Telehealth Technology, BIG Data);  Immunotherapy;  Surgical Oncology

Tumour Site

Presenters

Song-Bin Guo

Citation

Annals of Oncology (2024) 35 (suppl_2): S674-S711. 10.1016/annonc/annonc1596

Authors

S. Guo, X. Tian

Author affiliations

  • Deparment Of Medical Oncology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, 510060 - Guangzhou/CN

Resources

Login to get immediate access to this content.

If you do not have an ESMO account, please create one for free.

Abstract 1060P

Background

For cancer neoadjuvant and adjuvant immunotherapy, after several decades of evolution, the field currently possesses an enormous volume of underutilized data. Informatics analysis to thoroughly excavate the similarities and differences between the two is desperately necessary.

Methods

Extensive relevant studies (n=1373) on neoadjuvant and adjuvant immunotherapy from 2014-2023 were collected for quantitative, hierarchical clustering, and comparative analyses after vigorous quality control.

Results

Over the last decade, neoadjuvant and adjuvant immunotherapy enjoyed promising development status (Annual Growth Rate: 25.18% vs 6.52%) and global collaboration (International Co-authorships: 19.93% vs 19.84%). Unsupervised hierarchical clustering identified their dominant research clusters, in which Cluster 4: Balance of neoadjuvant immunotherapy efficacy and safety and Cluster 2: Adjuvant immunotherapy clinical trials are emerging research populations. Burst and regression curve analyses uncovered domain pivotal research signatures, including biomarkers (R2=0.6505, p=0.0086) in neoadjuvant scenarios, and tumor microenvironment (R2=0.5571, p=0.0209) in adjuvant scenarios. The Walktrap algorithm further revealed that "non-small cell lung cancer, immune checkpoint inhibitors, melanoma" and "melanoma, hepatocellular carcinoma, dendritic cells" (Relevance Percentage: 100% vs 100%, Development Percentage: 37.5% vs 17.1%) are extensively relevant to this field, but remain underdeveloped. Furthermore, comprehensive quantitative comparisons revealed that this field's spotlight on neoadjuvant immunotherapy overtook adjuvant immunotherapy entirely after 2020; such a qualitative finding will facilitate proper decision-making for subsequent research and avoid significant wastage of healthcare resources.

Conclusions

This cross-sectional study comparatively analyzed the fundamental metrological information in cancer neoadjuvant and adjuvant immunotherapy, identified their pivotal research signatures, and provided some substantial predictions for their subsequent preclinical and clinical research.

Clinical trial identification

Editorial acknowledgement

Legal entity responsible for the study

The authors.

Funding

Has not received any funding.

Disclosure

All authors have declared no conflicts of interest.

This site uses cookies. Some of these cookies are essential, while others help us improve your experience by providing insights into how the site is being used.

For more detailed information on the cookies we use, please check our Privacy Policy.

Customise settings
  • Necessary cookies enable core functionality. The website cannot function properly without these cookies, and you can only disable them by changing your browser preferences.