SAA1 and metabolomic signatures predict hyperprogression with immunotherapy in pan cancers

Dear Editor, Hyperprogression disease (HPD) has been identified as a special form of progression correlated to immune checkpoint inhibitor (ICI) treatment and is featured by a sudden and dramatic acceleration of tumour progression following ICI treatment, dramatically reducing survival time.1, 2 There are limited strategies to address this clinical dilemma.3 Thus, the implementation of valid predictive biomarkers is urgently needed. Unfortunately, there are few available biomarkers for identifying HPD. In this study, we met with an HPD patient. Briefly, the patient was diagnosed with metastatic nasopharyngeal carcinoma and treated with camrelizumab. Unfortunately, an unexpected and dramatic acceleration of tumour progression occurred after immunotherapy (Figure S1A,B), thus the patient was diagnosed with HPD. Importantly, we prospectively collected tumour tissue and dynamic blood samples (Table S1) throughout the entire treatment process, which provided support for exploring the potential mechanisms and biomarkers of HPD. Moreover, two patients without HPD with matched baselines were enrolled for comparative analysis. We performed a multidimensional analysis based on integrated tumour tissues and plasma data (Figure 1A). Genetic sequencing indicated that the gene mutations in plasma were highly concordant with those in tumour tissues (Table S2), suggesting that plasma ctDNA may be an effective alternative to testing for patients whose tissue biopsies are not obtained. Mutation assessment indicated that the neurofibromin 1 gene (NF1), NFKB inhibitor alpha gene (NFKBIA), and tumour protein p53 gene (TP53) mutations might be oncogenic mutations in HPD (Figure 1B). To characterize the mechanism underlying HPD, a protein-protein interaction network was constructed based on the mutated proteins (Figure S2A and Table S3). We further performed gene set enrichment analysis based on differential plasma proteins (Figure 1C, D and Tables S4 and S5). Three core functional classes, tumour progression, Th2 cell differentiation and metabolism, were identified (Figure 1E and Figure S2B). Activated tumour progression was expected,4 but the immune and metabolic disorders in HPD remain unclear. Dynamic changes of circulating immune cells in HPD showed an obvious increase in CD19+ B cells, and inhibitory CD4+CD25+ T cells after immunotherapy, but decreases in total T cells and CD8+ T cells (Figure 1F). Total T cells and CD8+ T cells tended to increase during ICI therapy but decreased after ICI therapy in patients without HPD (Figures S2C and S2D). Cytokine analysis revealed elevated levels of pro-proliferative cytokines and Th2 cytokines, while T-cell-activating cytokines obviously decreased (Figure 1G and Table S6). These data suggest a suppressed immune profile for HPD, which is consistent with previously reported immune tumour microenvironment alterations of HPD.5 Plasma metabolomics was performed to explore metabolic dysregulation in HPD (Figure 2A). Low-density lipoprotein (LDL) was significantly upregulated in HPD (Figure S3A–D). Orthogonal partial least squares-discriminant analysis (OPLS-DA) showed that plasma lipid metabolites can effectively differentiate HPD (Figure 2B,C). Many lipids were significantly dysregulated in HPD (Figure 2D,E). Partial correlation analysis with covariate adjustment for time was also performed (Figure 2F). Integrated with these results, we identified 15 key metabolites. Notably, these metabolites were predominantly concentrated in the LDL-6 lipoprotein (Figure 2G), which was reported to be associated with cancer.6 LDL-6 is a subtype of low-density lipoprotein with the smallest particle size and highest density (Figure S3E). The proportion of LDL-6 increased from 28.9% preimmunotherapy to 40.2% post-HPD, while the average LDL diameter significantly decreased (Figure 2H, I and Table S7). LDL-6 levels increased rapidly in HPD during ICIs therapy but remained stable or declined in patients without HPD (Figure 2J and Figure S3F–I). In addition, LDL-6 triglyceride (L6TG) increased throughout the whole process, especially after the early immunotherapy, and even beyond the upper limit (Figure 2K,L). However, because only one patient was included, additional samples are needed in the future to validate the predictive value of LDL-6 subfractions for HPD. Other than lipid metabolites, OPLS-DA of nonlipid metabolites clearly distinguish samples from HPD (Figure S4A). To screen vital nonlipid metabolites, we integrated the results of OPLS-DA (Figure S4A,B), differential metabolites (Figure S4C,D) and partial correlation analysis (Figure S4E). N, N-dimethyglycine (DMG) and histidine stood out (Figure S4F). Patients with HPD exhibited a deceased DMG (Figure S4G) and histidine level (Figure S4H). Further analysis revealed that the histidine began to decrease below the lower limit after two weeks of immunotherapy (Figure S4I). Histidine and DMG dysregulation might be another metabolic characteristic in HPD, but further studies with larger sample sizes are needed. Altogether, our findings revealed that metabolic dysregulation serves as an important characteristic of HPD, which provided a new insight into HPD mechanisms. Metabolic biomarkers are preferable for dynamic monitoring of HPD because of their liability to be influenced by metabolic diseases.7, 8 Thus, we aimed to discover a protein biomarker to identify HPD. We first screened 65 differential proteins based on plasma proteomics (Figure 3A). We subsequently constructed an interacted network of these differential proteins with HPD mutated proteins (Figure 3B), analyzed the correlations of these differential proteins with differential plasma metabolites (Figure 3C and Table S8) and evaluated the prognostic value of these proteins (Figure 3D and Figure S5). After these screenings, SAA1 was the only candidate (Figure 3E). SAA1 is expressed mainly in malignant cells with single-cell analysis (Figure 3F). Furthermore, the plasma SAA1 trend to increase during HPD throughout the whole process, and even started during the early period of ICIs treatment (Figure 3G,H). Due to the limitation of sample size, further studies with more samples are needed in the future to confirm these findings. A validation cohort with 10 patients with HPD and 21 patients without HPD was used to confirm the effectiveness of SAA1. The baseline characteristics between HPD and non-HPD groups exhibited no differences (Table S9). SAA1 is expressed in tumour cells and highly expressed in HPD in various cancers (Figure 4A,B). Excitedly, 83.3% of patients with high SAA1 expression ultimately developed HPD without the influence of infection or metabolic disease9, 10 (Figure 4C,D). Therefore, these data suggest that SAA1 is a promising biomarker for the prediction of HPD prediction in pan cancers. In summary, our study provides a novel biomarker for HPD prediction, suggesting that easy immunohistochemical staining of SAA1 to predict HPD effectively. Jian Guan and Laiyu Liu conceptualized the study; Xiaoqing Wang, Longshan Zhang and Liwei Liao designed and performed most of the experiments; Nan Li and Tingxi Tang performed and supervised the proteomics analysis; Jianda Sun, Zhenhua Zhou and Jihong Huang carried out and supervised metabolite analysis; Yang Liu and Yingqiao Wang carried out and supervised the single cell analysis and survival analysis; Zekai Chen, Hanbin Zhang, Yunming Tian, Xiuting Zheng, Yi Yuan and Linlin Xiao were engaged in sample processing and clinical follow-up; Xiaoqing Wang and Liwei Liao wrote the first version of the manuscript, Longshan Zhang and Jian Guan revised the manuscript. All authors have reviewed and agreed to the published version of the manuscript. This study was supported by the Clinical Research Startup Program of Southern Medical University by High-level University Construction Funding of Guangdong Provincial Department of Education (LC2016PY015 and LC2019ZD008); Clinical Research Program of Nanfang Hospital, Southern Medical University (2018CR021 and 2020CR025). The National Natural Science Foundation of China (Nos. 822 72729 and 82303684); The Natural Science Foundation of Guangdong Province (Nos. 2022A1515010509 and 2023A1515010285); Medical Scientific Research Foundation of Guangdong Province (B2021449). The authors thank all the study participants and their contribution to this research. The authors thank ProteinT for providing Advanced metabolomics measurement and plasma proteomics analysis. The authors declare no conflict of interest. The patients' consents for nasopharyngeal carcinoma were obtained and the patients' consents of validation cohort were waived following the ethics committee of Nanfang Hospital protocol review. The study was approved by the ethics committee of Nanfang Hospital, Southern Medical University (project number NFEC-2022-470). Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.