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.
Resources from the same session
927P - Preliminary results of the BROADEN study: Burden of human papillomavirus-related head and neck cancers
Presenter: Laia Alemany
Session: Poster session 03
928P - Radiomic analysis based on machine learning of multi-MR sequences to assess early treatment response in locally advanced nasopharyngeal carcinoma
Presenter: Lei Qiu
Session: Poster session 03
929P - Advanced laryngeal squamous cell carcinoma prognosis and machine learning insights
Presenter: Tala Alshwayyat
Session: Poster session 03
Resources:
Abstract
930P - Real-world data analysis of oncological outcomes in patients with pathological extranodal extension (ENE) in OSCC: A proposal to refine the pathological nodal staging system
Presenter: Abhinav Thaduri
Session: Poster session 03
931P - Deep learning models for predicting short-term efficacy in locally advanced nasopharyngeal carcinoma
Presenter: Kexin Shi
Session: Poster session 03
932P - Accuracy and prognostic implications of extranodal extension on radiologic imaging in HPV-positive oropharyngeal cancer (HNCIG-ENE): A multinational, real-world study
Presenter: Hisham Mehanna
Session: Poster session 03
933P - Prediction of survival in patients with head and neck merkel cell carcinoma: Statistical and machine-learning approaches
Presenter: Jehad Yasin
Session: Poster session 03
934P - Harnessing artificial intelligence on real-world data to predict recurrence in head and neck squamous cell carcinoma patients: The HNC-TACTIC study
Presenter: Hisham Mehanna
Session: Poster session 03
936P - Chronic pain in cancer survivors: Head and neck versus other cancers
Presenter: Rong Jiang
Session: Poster session 03