Osimertinib in combination with anti-angiogenesis therapy presents a promising option for osimertinib-resistant non-small cell lung cancer

Abstract

Background: Osimertinib has become standard care for epidermal growth factor receptor (EGFR)-positive non-small cell lung cancer (NSCLC) patients whereas drug resistance remains inevitable. Now we recognize that the interactions between the tumor and the tumor microenvironment (TME) also account for drug resistance. Therefore, we provide a new sight into post-osimertinib management, focusing on the alteration of TME.

Methods: We conducted a retrospective study on the prognosis of different treatments after osimertinib resistance. Next, we carried out in vivo experiment to validate our findings using a humanized mouse model. Furthermore, we performed single-cell transcriptome sequencing (scRNA-seq) of tumor tissue from the above treatment groups to explore the mechanisms of TME changes.

Results: Totally 111 advanced NSCLC patients have been enrolled in the retrospective study. The median PFS was 9.84 months (95% CI 7.0-12.6 months) in the osimertinib plus anti-angiogenesis group, significantly longer than chemotherapy (P = 0.012) and osimertinib (P = 0.003). The median OS was 16.79 months (95% CI 14.97-18.61 months) in the osimertinib plus anti-angiogenesis group, significantly better than chemotherapy (P = 0.026), the chemotherapy plus osimertinib (P = 0.021), and the chemotherapy plus immunotherapy (P = 0.006). The efficacy of osimertinib plus anlotinib in the osimertinib-resistant engraft tumors (R-O+A) group was significantly more potent than the osimertinib (R-O) group (P<0.05) in vitro. The combinational therapy could significantly increase the infiltration of CD4⁺ T cells (P<0.05), CD25⁺CD4⁺ T cells (P<0.001), and PD-1⁺CD8⁺ T cells (P<0.05) compared to osimertinib. ScRNA-seq demonstrated that the number of CD8⁺ T and proliferation T cells increased, and TAM.mo was downregulated in the R-O+A group compared to the R-O group. Subtype study of T cells explained that the changes caused by combination treatment were mainly related to cytotoxic T cells. Subtype study of macrophages showed that proportion and functional changes in IL-1β.mo and CCL18.mo might be responsible for rescue osimertinib resistance by combination therapy.

Conclusions: In conclusion, osimertinib plus anlotinib could improve the prognosis of patients with a progressed disease on second-line osimertinib treatment, which may ascribe to increased T cell infiltration and TAM remodeling via VEGF-VEGFR blockage.

Keywords: Anti-angiogenesis; Drug resistance; Osimertinib; Tumor microenvironment.

Fig. 1

Flow diagram of patient enrollment

Fig. 2

The analysis of mPFS in different post-osimertinib treatment groups. A The mPFS stratified by different post-osimertinib treatment. B Univariate COX regression analysis by baseline characteristics for mPFS. C Multivariate COX regression analysis by baseline characteristics for mPFS

Fig. 3

The analysis of mOS in different post-osimertinib treatment groups. A The mOS was stratified by different post-osimertinib treatments. B Univariate COX regression analysis by baseline characteristics for mOS

Fig. 4

Efficacy of osimertinib combined with anlotinib for osimertinib-resistant xenografts in vitro. A The weight curve of humanized mice in each group during treatment. B The tumor volume curve of humanized mice during treatment. C Measurement of fresh xenograft tumor tissues after the mice were euthanized at the end of treatment (*, P<0.05; **, P<0.01)

Fig. 5

Flow cytometry of immune cells in peripheral blood of mice demonstrated a successful immune reconstitution. A Gating strategy of lymphocytes including T cells and B cells. Total leukocytes were gated with CD45⁺, in total lymphocytes were gated with CD3⁺. Then the CD4⁺ helper T cells, CD8⁺ cytotoxic T cells, and CD19⁺ B cells were then delineated in CD3⁺ lymphocytes, respectively. Next, CD4⁺ T cells were further divided into early activation (CD69⁺), middle activation (CD25⁺), late activation (HLA-DR⁺); exhausted (PD1⁺/CTLA-4⁺/TIM3^+/TIGIT⁺), as well as immunosuppressive Treg (FOXP3⁺CD25⁺) T cells. Similarly, we also applied the above gating strategy in CD8⁺T cells. In addition, we checked IFN-γ and Granzyme B, which were related to killing functions. B Gating strategy of myeloid cells involving macrophages and MDSCs. Among CD45⁺ leukocytes, overall myeloid lineage cells were delineated with CD11b⁺. Total macrophages were gated with CD68⁺; classically activated M1 macrophages were gated with CD86⁺; conditionally activated M2 macrophages were gated with CD206⁺; and immunosuppressive MDSCs were circled with HLA-DR-CD33⁺. C Comparison of the proportion of major cell components of tumor-infiltrating immune cells in each treatment group

Fig. 6

Flow cytometry detection of infiltrating immune cells in tumors. A In addition to the panel used in blood, we further detected cytokines secreted by CD68+ macrophages in tumors. B Analysis of tumor-infiltrating immune cells. C Comparative analysis of tumor-infiltrating T cells. D Tumor-infiltrating macrophages and their functional analysis

Fig. 7

Cell type annotation and function enrichment analysis by scRNA sequencing of tumor tissues. A Cell Clusters Visualization by UMAP dimension reduction analysis. B Specific markers for cell annotation. C Heatmap of Top5 differential genes between cell types. D Function enrichment analyses on DEGs

Fig. 8

Differences in TME between R-O and S-O groups and between R-O+A and R-O groups. A The relative abundance of cell components was compared. B Differences in cell communication among the above cells. C Differences in function enrichment in those cell types

Fig. 9

T cells subtype analysis between R-O and S-O groups and between R-O+A and R-O groups. A T Cell subtype cluster visualization by UMAP dimension reduction analysis. B Function clusters of specific markers for subtype annotation. C The relative abundance of various subtype T cell components was compared. D Differences in function enrichment were noted in those cell subtypes

Fig. 10

Macrophage subtype analysis between R-O and S-O groups and between R-O+A and R-O groups. A Macrophage subtype cluster visualization by UMAP dimension reduction analysis. B Function clusters of specific markers for M1 type macrophage annotation. C Function clusters of specific markers for M2 type macrophage annotation. D The relative abundance of various subtype macrophages was compared. E Differences in function enrichment in those cell subtypes were also noted