A Preliminary Study of the Complement Component 1q Levels in Predicting the Efficacy of Combined Immunotherapy in Patients with Lung Cancer

Abstract

Objective To evaluate the value of serum complement component 1q (C1q) levels in predicting the efficacy of combined immunotherapy in patients with lung cancer. Methods A total of 42 patients with lung cancer who received combined immunotherapy in the cancer center of Renmin Hospital of Wuhan University were included in this study. The clinical data of serum C1q and lactate dehydrogenase (LDH) levels before and three weeks after immunotherapy were collected. Results Response evaluation showed that the number of patients with complete response (CR), partial response (PR), stable disease (SD), and progressive disease (PD) was 0 (0%), 26 (61.9%), 14 (33.3%), and 2 (4.8%), respectively. The CR/PR group (patients with CR or PR) showed higher p C1q (C1q level before immunotherapy) and i C1q (C1q level 3 weeks after immunotherapy) than the SD/PD group (patients with SD or PD). The LDH reduction (96.2%) and C1q increment (84.6%) in the CR/PR group 3 weeks after immunotherapy were higher than those of the SD/PD group, and the differences were statistically significant. Logistic regression analysis indicated that p C1q, i C1q, and LDH level trends 3 weeks after the treatment were significantly correlated to the efficacy of combined immunotherapy with odds ratios of 8.185, 5.500, and 0.031, respectively. Conclusion High C1q levels before immunotherapy and increased C1q levels and decreased LDH levels 3 weeks afterward suggest good therapeutic effects of combined immunotherapy in patients with lung cancer. Serum C1q levels have certain clinical significance in predicting the efficacy of combined immunotherapy.

Identification of plasma proteins associated with oesophageal cancer chemotherapeutic treatment outcomes using SWATH-MS

“…Other proteomic studies have identified C1q as a potential prognostic indicator. In lung and kidney cancer and in glioma, higher C1q levels are associated with a negative prognosis [25,26]. Conversely, in breast cancer and some types of lung cancer, as with our study, higher C1q levels are associated with a more positive prognosis [27,28].…”

Advances in artificial intelligence to predict cancer immunotherapy efficacy

“…( 51 ) suggested combining DNA and RNA sequencing, immunohistochemical staining results, demographic baseline data, medical history, and laboratory test results to analyze the information related to efficacy and break the limitation of relying only on PD-1 expression level and TMB level to predict immunotherapy response, and develop a mature model for immunotherapy response prediction for Chinese lung cancer patients. Along with PD-L1 expression, TMB, and TIL, it has been demonstrated that miRNA abnormalities ( 62 ), EGFR mutations ( 54 ), TP53 mutations ( 63 ), ALK rearrangements ( 55 ), gut microbiota ( 56 ), Fc gamma receptor (FcγR) polymorphisms ( 57 ) and serum complement levels ( 58 ) all influence immunotherapy response to some extent. It would facilitate the robustness of prediction if this information could be merged with pathological sections and imaging histology to form multi-omics integrated data.…”

Clinical predictors of treatment efficacy and a prognostic nomogram in patients with lung adenocarcinoma receiving immune checkpoint inhibitors: a retrospective study

“…However, identifying the patient cohort that will benefit from immunotherapy remains a pressing problem to be addressed. Previous studies have explored the prediction of immunotherapy efficacy from multiple perspectives, such as drug-based score ( 12 ), lung immune prognostic index (LIPI) ( 13 ), smoking ( 14 ), liver metastasis ( 15 ), lactate dehydrogenase (LDH) ( 16 , 17 ), tumor mutational burden (TMB) expression ( 18 ), neutrophil-to-lymphocyte ratio (NLR) ( 19 , 20 ), modified lung immune predictive index (mLIPI) scores ( 21 ), and so on. Despite achieving some results, the existing literature does not completely solve the problem of predicting immunotherapy efficacy.…”