Prediction of neoadjuvant chemotherapeutic efficacy in patients with locally advanced gastric cancer by serum IgG glycomics profiling

Qin, Ruihuan; Yang, Yupeng; Chen, Hao; Qin, Wenjun; Han, Jing; Gu, Yong; Pan, Yiqing; Cheng, Xi; Zhao, Junjie; Wang, Xuefei; Ren, Shifang; Sun, Yihong; Gu, Jianxin

doi:10.1186/s12014-020-9267-8

Cited by 20 publications

(16 citation statements)

References 30 publications

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“…A recently published study suggested that the immunoScore signature could predict response to adjuvant chemotherapy in stage II and III GC patients in subgroup analysis, but the result was not validated [ 28 ]. Another two studies reported that serum biomarkers based model in GC patients from single-center could classify the patients who are suitable for chemoradiation or chemotherapy [ 29 , 30 ]. However, these models were conducted in a small sample size and were not independently validated.…”

Section: Discussionmentioning

confidence: 99%

A predictive signature for oxaliplatin and 5-fluorouracil based chemotherapy in locally advanced gastric cancer

Wang

Liu

Chen

et al. 2021

Translational Oncology

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Adjuvant chemotherapy(AC) plays a substantial role in the treatment of locally advanced gastric cancer (LAGC), but the response remains poor. We aims to improve its efficacy in LAGC. Therefore, we identified the expression of eight genes closely associated with platinum and fluorouracil metabolism ( RRM1 , RRM2 , RRM2B , POLH , DUT , TYMS , TYMP , MKI67 ) in the discovery cohort (N=291). And we further validated the findings in TCGA (N=279) and GEO. Overall survival (OS) was used as an endpoint. Univariate and multivariate Cox models were applied. A multivariate Cox regression model was simulated to predict the OS. In the discovery cohort, the univariate Cox model indicated that AC was beneficial to high-RRM1, high-DUT, low-RRM2, low-RRM2B, low-POLH, low-KI67, low-TYMS or low-TYMP patients, the results were validated in the TCGA cohort. The multivariate Cox model showed consistent results. Cumulative analysis indicated that patients with low C-Score respond poorly to the AC, whereas the high and medium C-Score patients significantly benefit from AC. A risk model based on the above variables successfully predicted the OS in both cohorts (AUC=0.75 and 0.67, respectively). Further validation in a panel of gastric cancer cell (GC) lines (N=37) indicated that C-Score is significantly associated with IC50 value to fluorouracil. Mutation profiling showed that C-Score was associated with the number and types of mutations. In conclusion, we successfully simulated a predictive signature for the efficacy of AC in LAGC patients and further explored the potential mechanisms. Our findings could promote precision medicine and improve the prognosis of LAGC patients.

show abstract

Section: Discussionmentioning

confidence: 99%

A predictive signature for oxaliplatin and 5-fluorouracil based chemotherapy in locally advanced gastric cancer

Wang

Liu

Chen

et al. 2021

Translational Oncology

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“…IgG glycosylation has already been investigated in various cancer types, often revealing associations with patient survival rate, response to therapy and diagnostic markers. For instance, in gastric cancer patients, a high level of digalactosylated IgG 2 glycoforms was shown to be an indicator of better survival (40), whereas an IgG glycosylation-based model proposed by Qin et al (46) could accurately predict response to neoadjuvant therapy. In turn, in prostate cancer, the ratio of IgG agalactosylated structures to the sum of monogalactosylated and digalactosylated ones strongly correlated with levels of prostate-specific antigen (PSA), which is the most prevalently used prostate cancer biomarker (47).…”

Section: Discussionmentioning

confidence: 99%

Immunoglobulin G Subclass-Specific Glycosylation Changes in Primary Epithelial Ovarian Cancer

et al. 2020

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Epithelial ovarian cancer (EOC) was previously shown to be associated with glycosylation changes of total serum and total IgG proteins. However, as a majority of previous studies analyzed released glycan profiles, still little is known about IgG subclass-specific alterations in ovarian cancer. Hence, in this study, we investigated EOC-related glycosylation changes of the three most abundant IgG subclasses, namely, IgG 1 , IgG 2 and IgG 3 isolated from sera of 87 EOC patients and 74 agematched healthy controls. In order to separate IgG 2 and IgG 3 , we performed a two-step affinity purification employing Protein A and Protein G Sepharose. After tryptic digestion, IgG glycopeptides were enriched and measured by MALDI-TOF-MS. Finally, EOC-related glycosylation changes were monitored at the level of total agalactosylation, monogalactosylation, digalactosylation, sialylation, bisection and fucosylation, which were calculated separately for each IgG subclass. Interestingly, aside from an EOC-related increase in agalactosylation/decrease in monogalactosylation and digalactosylation observed in all IgG subclasses, some subclass-specific trends were detected. Glycosylation of IgG 1 was found to be most strongly affected in EOC, as it exhibited the highest number of significant differences between healthy controls and EOC patients. Specifically, IgG 1 was the only subclass that showed a significant decrease in sialylation and a significant increase in fucosylation in EOC patients. Interestingly, IgG 2 and IgG 3 that were often investigated collectively in previous studies, were found to have distinct glycosylation patterns. IgG 3 displayed stronger EOC-related increase in agalactosylation/decrease in digalactosylation and was characterized by notably higher sialylation, which consequently decreased in EOC patients. In conclusion, our study indicates that IgG subclasses exhibit subtly distinct glycosylation patterns of EOC-related alterations and that IgG 1 and IgG 3 agalactosylation show the strongest association with CA125, the routine diagnostic marker. Additionally, our results show that simultaneous analyses of IgG 2 and IgG 3 might lead to wrong conclusions as these two subclasses exhibit noticeably different glycosylation phenotypes.

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“…A recently published study suggested that the immunoScore signature could predict response to adjuvant chemotherapy in stage II and III GC patients in subgroup analysis, but the result was not validated (27). Another two studies reported that serum biomarkers based model in GC patients from single-center could classify the patients who are suitable for chemoradiation or chemotherapy (28,29). However, these models were conducted in a small sample size and were not independently validated.…”

Section: C-score Is An Ideal Predictor For Adjuvant Therapy E Cacy Inmentioning

confidence: 99%

A Predictive Signature for Oxaliplatin and 5-fluorouracil Based Chemotherapy Inlocally Advanced Gastric Cancer

Wang

Liu

Chen

et al. 2020

Preprint

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Background: Adjuvantchemotherapy(AC)plays a substantial role in the treatment of locally advanced gastric cancer (LAGC), but the response remains poor. Weaims to improve its efficacy in LAGC. Methods: We identified the expression of eight genes closely associated with platinum and fluorouracil metabolism (RRM1, RRM2, RRM2B, POLH, DUT, TYMS, TYMP, MKI67) in the discovery cohort (N=291). And we further validated the findings in TCGA (N=279) and GEO. Overall survival (OS) was used as an endpoint. Univariate and multivariate Cox models were applied. A multivariate Cox regression model was simulated to predict theOS. Results: In the discovery cohort,the univariate Coxmodelindicated that AC was beneficial to high-RRM1, high-DUT, low-RRM2, low-RRM2B, low-POLH, low-KI67, low-TYMS or low-TYMP patients, the results were validated in the TCGA cohort. The multivariate Cox model showed consistent results. Cumulative analysis indicated that patients with low C-Score respond poorly to the AC, whereas thehigh and mediumC-Score patients significantly benefit from AC. A risk model based on the above variables successfully predicted the OS in both cohorts (AUC=0.75 and 0.67, respectively). Further validation in a panel of gastric cancer cell (GC) lines (N=37) indicated that C-Score is significantly associated with IC50 value to fluorouracil. Mutation profiling showed that C-Score was associated with the number and types of mutations. Conclusion: In this study, we successfully simulated a predictive signature for the efficacy of AC in LAGC patients and further explored the potential mechanisms. Our findings could promote precision medicine and improve the prognosis of LAGC patients.

show abstract

Prediction of neoadjuvant chemotherapeutic efficacy in patients with locally advanced gastric cancer by serum IgG glycomics profiling

Cited by 20 publications

References 30 publications

A predictive signature for oxaliplatin and 5-fluorouracil based chemotherapy in locally advanced gastric cancer

A predictive signature for oxaliplatin and 5-fluorouracil based chemotherapy in locally advanced gastric cancer

Immunoglobulin G Subclass-Specific Glycosylation Changes in Primary Epithelial Ovarian Cancer

A Predictive Signature for Oxaliplatin and 5-fluorouracil Based Chemotherapy Inlocally Advanced Gastric Cancer

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