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A high risk of local relapse is the main challenge of HER2+ breast cancer after breast-conserving surgery. We aimed to develop a long-acting delivery system for Herceptin, a HER2-targeting antibody, using injectable and thermosensitive hydrogels as the carrier to prevent the local relapse of HER2+ breast tumors while minimizing systemic side effects, especially cardiotoxicity.Methods: Two poly(lactic acid-co-glycolic acid)-b-poly(ethylene glycol)-b-poly(lactic acid-co-glycolic acid) (PLGA-PEG-PLGA) triblock copolymers with different PEG/PLGA proportions were synthesized. Their mixtures with rational mix proportions displayed sol-gel transitions in water with rising of temperature and the Herceptin-loaded hydrogel systems were then prepared. Both the in vivo antitumor and anti-relapse efficacies were evaluated after hypodermic injection of the Herceptin-loaded hydrogel, and the cardiotoxicity was also detected.Results: The gel performance, degradation rate and drug release kinetics of hydrogels were easily adjustable by simply varying the mix proportion. The hydrogel matrix with a specific mix proportion not only avoided initial burst release but also achieved sustained release of Herceptin in vitro for up to 80 days, which is the longest period of Herceptin delivery that has ever been reported. In vivo biodistribution studies performed in SK-BR-3 tumor-bearing mice revealed that a single hypodermic administration of the Herceptin-loaded hydrogel adjacent to the tumor tissue promoted the intratumoral antibody accumulation. This resulted in a better antitumor efficacy compared to weekly hypodermic injections of Herceptin solution for 28 days. A tumor relapse model was also established by imitative breast-conserving surgery on tumor-bearing mice, and both the single injection of the Herceptin-loaded hydrogel and the weekly injection of the Herceptin solution achieved superior anti-relapse efficacy. Furthermore, both antitumor and anti-relapse experiments demonstrated that the weekly pulsed administration of the Herceptin solution caused cardiotoxicity; however, the sustained release of Herceptin from the hydrogel effectively prevented this side effect.Conclusion: The Herceptin-loaded hydrogel has great potential for preventing the relapse of HER2+ breast tumors after breast-conserving surgery with enhanced therapeutic efficacy, improved patient compliance and significantly reduced side effects.

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BackgroundImmune checkpoint blockade (ICB) has been approved for the treatment of triple-negative breast cancer (TNBC), since it significantly improved the progression-free survival (PFS). However, only about 10% of TNBC patients could achieve the complete response (CR) to ICB because of the low response rate and potential adverse reactions to ICB.MethodsOpen datasets from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) were downloaded to perform an unsupervised clustering analysis to identify the immune subtype according to the expression profiles. The prognosis, enriched pathways, and the ICB indicators were compared between immune subtypes. Afterward, samples from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) dataset were used to validate the correlation of immune subtype with prognosis. Data from patients who received ICB were selected to validate the correlation of the immune subtype with ICB response. Machine learning models were used to build a visual web server to predict the immune subtype of TNBC patients requiring ICB.ResultsA total of eight open datasets including 931 TNBC samples were used for the unsupervised clustering. Two novel immune subtypes (referred to as S1 and S2) were identified among TNBC patients. Compared with S2, S1 was associated with higher immune scores, higher levels of immune cells, and a better prognosis for immunotherapy. In the validation dataset, subtype 1 samples had a better prognosis than sub type 2 samples, no matter in overall survival (OS) (p = 0.00036) or relapse-free survival (RFS) (p = 0.0022). Bioinformatics analysis identified 11 hub genes (LCK, IL2RG, CD3G, STAT1, CD247, IL2RB, CD3D, IRF1, OAS2, IRF4, and IFNG) related to the immune subtype. A robust machine learning model based on random forest algorithm was established by 11 hub genes, and it performed reasonably well with area Under the Curve of the receiver operating characteristic (AUC) values = 0.76. An open and free web server based on the random forest model, named as triple-negative breast cancer immune subtype (TNBCIS), was developed and is available from https://immunotypes.shinyapps.io/TNBCIS/.ConclusionTNBC open datasets allowed us to stratify samples into distinct immunotherapy response subgroups according to gene expression profiles. Based on two novel subtypes, candidates for ICB with a higher response rate and better prognosis could be selected by using the free visual online web server that we designed.