Dataset Information

Using a panel of multiple tumor-associated antigens to enhance autoantibody detection for immunodiagnosis of gastric cancer.

ABSTRACT: Autoantibodies against tumor-associated antigens (TAAs) are attractive non-invasive biomarkers for detection of cancer due to their inherently stable in serum. Serum autoantibodies against 9 TAAs from gastric cancer (GC) patients and healthy controls were measured by enzyme-linked immunosorbent assay (ELISA). A logistic regression model predicting the risk of being diagnosed with GC in the training cohort (n = 558) was generated and then validated in an independent cohort (n = 372). Area under the receiver operating characteristic curve (AUC) was used to assess the diagnostic performance. Finally, an optimal prediction model with 6 TAAs (p62, c-Myc, NPM1, 14-3-3ξ, MDM2 and p16) showed a great diagnostic performance of GC with AUC of 0.841 in the training cohort and 0.856 in the validation cohort. The proportion of subjects being correctly defined were 78.49% in the training cohort and 81.99% in the validation cohort. This prediction model could also differentiate early-stage (stage I-II) GC patients from healthy controls with sensitivity/specificity of 76.60%/72.34% and 80.56%/79.17% in the training and validation cohort, respectively, and the overall sensitivity/specificity for early-stage GC were 78.92%/74.70% when being combined with two cohorts. This prediction model presented no significant difference for the diagnostic accuracy between early-stage and late-stage (stage III - IV) GC patients. The model with 6 TAAs showed a high diagnostic performance for GC detection, particularly for early-stage GC. This study further supported the hypothesis that a customized array of multiple TAAs was able to enhance autoantibody detection in the immunodiagnosis of GC.

SUBMITTER: Wang S

PROVIDER: S-EPMC6136883 | biostudies-other | 2018

REPOSITORIES: biostudies-other

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Using a panel of multiple tumor-associated antigens to enhance autoantibody detection for immunodiagnosis of gastric cancer.

Wang Shuaibing S Qin Jiejie J Ye Hua H Wang Keyan K Shi Jianxiang J Ma Yan Y Duan Yitao Y Song Chunhua C Wang Xiao X Dai Liping L Wang Kaijuan K Wang Peng P Zhang Jianying J

Oncoimmunology 20180418 8

Autoantibodies against tumor-associated antigens (TAAs) are attractive non-invasive biomarkers for detection of cancer due to their inherently stable in serum. Serum autoantibodies against 9 TAAs from gastric cancer (GC) patients and healthy controls were measured by enzyme-linked immunosorbent assay (ELISA). A logistic regression model predicting the risk of being diagnosed with GC in the training cohort (n = 558) was generated and then validated in an independent cohort (n = 372). Area under t ...[more]

PMID: 30221047

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Project description:Lung cancer is the second most frequent malignancy and the leading cause of cancer-associated death worldwide. Compared with patients diagnosed at advanced disease stages, early detection of lung cancer significantly improved the 5-year survival rate from 3.3% to 48.8%, which highlights the importance of early detection. Although multiple technologies have been applied to the screening and early diagnosis of lung cancer so far, some limitations still exist so they could not fully suit the needs for clinical application. Evidence show that autoantibodies targeting tumor-associated antigens(TAAs) could be found in the sera of early-stage patients, and they are of great value in diagnosis. Methods, we identified and screened TAAs in early-stage non-small cell lung cancer(NSCLC) samples using the serological analysis of recombinant cDNA expression libraries(SEREX). We measured the levels of the 36 autoantibodies targeting TAAs obtained by preliminary screening via liquid chip technique in the training set(332 serum samples from early-stage NSCLC patients, 167 samples from patients with benign lung lesions, and 208 samples from patients with no obvious abnormalities in lungs), and established a binary logistic regression model based on the levels of 8 autoantibodies to distinguish NSCLC samples. Results, We validated the diagnostic efficacy of this model in an independent test set(163 serum samples from early-stage NSCLC patients, and 183 samples from patients with benign lung lesions), the model performed well in distinguishing NSCLC samples with an AUC of 0.8194. After joining the levels of 4 serum tumor markers into its independent variables, the final model reached an AUC of 0.8568, this was better than just using the 8 autoantibodies (AUC:0.8194) or the 4 serum tumor markers alone(AUC: 0.6948). In conclusion, we screened and identified a set of autoantibodies in the sera of early-stage NSCLC patients through SEREX and liquid chip technique. Based on the levels of 8 autoantibodies, we established a binary logistic regression model that could diagnose early-stage NSCLC with high sensitivity and specificity, and the 4 conventional serum tumor markers were also suggested to be effective supplements for the 8 autoantibodies in the early diagnosis of NSCLC.

Project description:BackgroundCirculating tumor DNA (ctDNA) carries information on tumor burden. However, the mutation spectrum is different among tumors. This study was designed to examine the utility of ctDNA for monitoring tumor burden based on an individual mutation profile.MethodologyDNA was extracted from a total of 176 samples, including pre- and post-operational plasma, primary tumors, and peripheral blood mononuclear cells (PBMC), from 44 individuals with colorectal tumor who underwent curative resection of colorectal tumors, as well as nine healthy individuals. Using a panel of 50 cancer-associated genes, tumor-unique mutations were identified by comparing the single nucleotide variants (SNVs) from tumors and PBMCs with an Ion PGM sequencer. A group of the tumor-unique mutations from individual tumors were designated as individual marker mutations (MMs) to trace tumor burden by ctDNA using droplet digital PCR (ddPCR). From these experiments, three major objectives were assessed: (a) Tumor-unique mutations; (b) mutation spectrum of a tumor; and (c) changes in allele frequency of the MMs in ctDNA after curative resection of the tumor.ResultsA total of 128 gene point mutations were identified in 27 colorectal tumors. Twenty-six genes were mutated in at least 1 sample, while 14 genes were found to be mutated in only 1 sample, respectively. An average of 2.7 genes were mutated per tumor. Subsequently, 24 MMs were selected from SNVs for tumor burden monitoring. Among the MMs found by ddPCR with > 0.1% variant allele frequency in plasma DNA, 100% (8 out of 8) exhibited a decrease in post-operation ctDNA, whereas none of the 16 MMs found by ddPCR with < 0.1% variant allele frequency in plasma DNA showed a decrease.ConclusionsThis panel of 50 cancer-associated genes appeared to be sufficient to identify individual, tumor-unique, mutated ctDNA markers in cancer patients. The MMs showed the clinical utility in monitoring curatively-treated colorectal tumor burden if the allele frequency of MMs in plasma DNA is above 0.1%.

Dataset Information

Using a panel of multiple tumor-associated antigens to enhance autoantibody detection for immunodiagnosis of gastric cancer.

Publications

Using a panel of multiple tumor-associated antigens to enhance autoantibody detection for immunodiagnosis of gastric cancer.

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