Comparative investigation of neoadjuvant immunotherapy versus adjuvant immunotherapy in perioperative patients with cancer: a global-scale, cross-sectional, and large-sample informatics study

Abstract

Background: Neoadjuvant and adjuvant immunotherapies for cancer have evolved through a series of remarkable and critical research advances; however, addressing their similarities and differences is imperative in clinical practice. Therefore, this study aimed to examine their similarities and differences from the perspective of informatics analysis.

Methods: This cross-sectional study retrospectively analyzed extensive relevant studies published between 2014 and 2023 using stringent search criteria, excluding nonpeer-reviewed and non-English documents. The main outcome variables are publication volume, citation volume, connection strength, occurrence frequency, relevance percentage, and development percentage. Furthermore, an integrated comparative analysis was conducted using unsupervised hierarchical clustering, spatiotemporal analysis, regression statistics, and Walktrap algorithm analysis.

Results: This analysis included 1373 relevant studies. Advancements in neoadjuvant and adjuvant immunotherapies have been promising over the last decade, with an annual growth rate of 25.18 vs. 6.52% and global collaboration (International Co-authorships) of 19.93 vs. 19.84%. Respectively, five dominant research clusters were identified through unsupervised hierarchical clustering based on machine learning, among which Cluster 4 (Balance of neoadjuvant immunotherapy efficacy and safety) and Cluster 2 (Adjuvant immunotherapy clinical trials) [Average Publication Year (APY): 2021.70±0.70 vs. 2017.54±4.59] are emerging research populations. Burst and regression curve analyses uncovered domain pivotal research signatures, including microsatellite instability (R 2 =0.7500, P =0.0025) and biomarkers (R 2 =0.6505, P =0.0086) in neoadjuvant scenarios, and the tumor microenvironment (R 2 =0.5571, P =0.0209) in adjuvant scenarios. The Walktrap algorithm further revealed that 'neoadjuvant immunotherapy, nonsmall cell lung cancer (NSCLC), immune checkpoint inhibitors, melanoma' and 'adjuvant immunotherapy, melanoma, hepatocellular carcinoma, dendritic cells' (Relevance Percentage: 100 vs. 100%, Development Percentage: 37.5 vs. 17.1%) are extremely relevant to this field but remain underdeveloped, highlighting the need for further investigation.

Conclusion: This study identified pivotal research signatures and provided substantial predictions for neoadjuvant and adjuvant cancer immunotherapies. In addition, comprehensive quantitative comparisons revealed a notable shift in focus within this field, with neoadjuvant immunotherapy taking precedence over adjuvant immunotherapy after 2020; such a qualitative finding facilitate proper decision-making for subsequent research and mitigate the wastage of healthcare resources.

Figure 1

Visualization overview of metrological information in the field of neoadjuvant and adjuvant immunotherapies in oncology. (A) Three stages in the evolutionary patterns of neoadjuvant immunotherapy. (B) Three stages in the evolutionary patterns of adjuvant immunotherapy. Based on the metrological quantities and key events, the field of cancer immunotherapy was divided into three phases from 2000 to 2022: the embryonic stage, the stable development stage, and the exponential growth stage. (C) Global and local citations for the citation classics in the field of neoadjuvant immunotherapy. (D) Global and local citations for the citation classics in the field of adjuvant immunotherapy. (E) Annual citations of the citation classics in the field of neoadjuvant immunotherapy. (F) Annual citations of the citation classics in the field of adjuvant immunotherapy.

Figure 2

Metrological comparative investigation of the prolific scholars or journal leaders in neoadjuvant and adjuvant immunotherapies. (A) Total publications by the prolific scholars in neoadjuvant immunotherapy. (B) Total citations by the prolific scholars in neoadjuvant immunotherapy. (C) Total publications by the prolific scholars in adjuvant immunotherapy. (D) Total citations by the prolific scholars in adjuvant immunotherapy. (E) Annual publication volume by the prolific scholars in neoadjuvant immunotherapy. (F) Annual citation volume by the prolific scholars in neoadjuvant immunotherapy. (G) Annual publication volume by the prolific scholars in adjuvant immunotherapy. (H) Annual citation volume by the prolific scholars in adjuvant immunotherapy. (I) Citation ray diagrams for the journal leaders in neoadjuvant immunotherapy. Pretriple categorization by publication volume. AO, Annals of Oncology; CR, Cancer Research; CCR, Clinical Cancer Research; JIC, Journal for Immunotherapy of Cancer; JCO, Journal of Clinical Oncology; JTO, Journal of Thoracic Oncology; NM, Nature Medicine; NEJM, New England Journal of Medicine; N, Nature; S, Science. (J) Citation ray diagrams for the journal leaders in adjuvant immunotherapy. Pretriple categorization by publication volume. AO, Annals of Oncology; CR, Cancer Research; CCR, Clinical Cancer Research; CII, Cancer Immunology Immunotherapy; JEM, Journal of Experimental Medicine; JI, Journal of Immunology; JCO, Journal of Clinical Oncology; L, Lancet; LO, Lancet Oncology; N, Nature. (K) Proportional river chart of journal leaders in neoadjuvant immunotherapy. (L) Proportional river chart of journal leaders in adjuvant immunotherapy.

Figure 3

Spatial and temporal distribution and interactions of the prolific countries and affiliations in neoadjuvant immunotherapy and adjuvant immunotherapies. (A) Total publications by the prolific countries in neoadjuvant immunotherapy. (B) Total citations by the prolific countries in neoadjuvant immunotherapy. (C) Metrological comparison of publication and citation volume by the prolific countries in neoadjuvant immunotherapy. (D) Total publications by the prolific countries in adjuvant immunotherapy. (E) Total citations by the prolific countries in adjuvant immunotherapy. (F) Metrological comparison of publication and citation volume by the prolific countries in adjuvant immunotherapy. (G) Annual publications by the prolific countries in neoadjuvant immunotherapy. (H) Annual publications by the prolific countries in adjuvant immunotherapy. (I) Spatial interactions of the prolific countries in neoadjuvant immunotherapy. (J) Spatial interactions of the prolific countries in adjuvant immunotherapy. (K) National attribution and annual publications of the prolific affiliations in neoadjuvant immunotherapy. (L) National attribution and annual publications of the prolific affiliations in adjuvant immunotherapy.

Figure 4

Comparative investigation of spatial and temporal networks of research signatures in the fields of neoadjuvant immunotherapy and adjuvant immunotherapies. (A) Unsupervised learning hierarchical clustering of research signatures in neoadjuvant immunotherapy. (B) Unsupervised learning hierarchical clustering of research signatures in adjuvant immunotherapy. (C) Temporal distribution pattern of research signatures in neoadjuvant immunotherapy. (D) Temporal distribution pattern of research signatures in adjuvant immunotherapy. (E) Spatial density network based on connection frequency for research signatures in neoadjuvant immunotherapy. (F) Spatial density network based on connection frequency for research signatures in adjuvant immunotherapy. (G) Spatial density network based on occurrence frequency for research signatures in neoadjuvant immunotherapy. (H) Spatial density network based on occurrence frequency for research signatures in adjuvant immunotherapy.

Figure 5

Burst status, temporal evolution, and regression curve of research signatures in neoadjuvant and adjuvant immunotherapies. (A) Burst status and temporal evolution of research signatures in neoadjuvant immunotherapy. (B) Populations of annual frequency-based regression models for research signatures in neoadjuvant immunotherapy. ‘a’ denotes the slope; ‘R²’ denotes the goodness of fit (‘P<0.05’ indicates statistical significance). (C) Burst status and temporal evolution of research signatures in adjuvant immunotherapy. (D) Populations of annual frequency-based regression models for research signatures in adjuvant immunotherapy.

Figure 6

Potentiality comparison investigation of research signatures in neoadjuvant immunotherapy and adjuvant immunotherapies. (A) Potentiality discovery of research signatures in neoadjuvant immunotherapy. (B) Potentiality discovery of research signatures in adjuvant immunotherapy.