Oops, you're using an old version of your browser so some of the features on this page may not be displaying properly.

MINIMAL Requirements: Google Chrome 24+Mozilla Firefox 20+Internet Explorer 11Opera 15–18Apple Safari 7SeaMonkey 2.15-2.23

Poster Display session 3

5678 - Nanomaterials Augmented LDI-TOF-MS for Hepatocellular Carcinoma Diagnosis and Classification

Date

30 Sep 2019

Session

Poster Display session 3

Topics

Translational Research

Tumour Site

Presenters

Jian Zhou

Citation

Annals of Oncology (2019) 30 (suppl_5): v574-v584. 10.1093/annonc/mdz257

Authors

J. Zhou1, W. Liu1, L. Zhao2, J. Jiang2, Y. Lu2, J. Hu1, H. Zhang3, J. Bai4, X. Li2

Author affiliations

  • 1 Liver Surgery And Transplantation, Zhongshan Hospital, Fudan University, 200032 - Shanghai/CN
  • 2 Endocrinology And Metabolism, Zhongshan Hospital, Fudan University, 200032 - Shanghai/CN
  • 3 National Engineering Research Center For Biomaterials, Sichuan University, 610000 - Chengdu/CN
  • 4 Pharmaceutical Sciences, Tsinghua University, 100084 - Beijing/CN

Resources

Login to access the resources on OncologyPRO.

If you do not have an ESMO account, please create one for free.

Abstract 5678

Background

Hepatocellular carcinoma (HCC) has relatively sensitive and specific serum tumor antigen markers (AFP), which is also the most common serological marker for cancer screening. However, there are unignorable limitations, including possible false-negatives/positives owing to confounding conditions. Reliable non-invasive diagnostics is still in urgent need. This work proposes a novel LDI-TOF-MS technique for HCC screening and diagnosis. By taking advantage of 3D nanostructures and machine learning, our technique enables high fidelity and reproducibility.

Methods

An LDI-TOF-MS platform was established for HCC screening and was applied to 139 patients with liver cancer, as well as 203 healthy controls (Table). All mass spectrum was collected within a mass range of 100 to 1,100 Da for metabolites. Based on the data acquired by LDI-TOF-MS, SVM algorithm was developed and applied for automated cancer classification across six cancer types, which was further validated by single blinded samples with randomly selected cancer patients and controls.Table: 1432P

Summary of patient and healthy control characteristics

Patient TypeNGenderGenderAgeAJCC StageAJCC StageAJCC StageAJCC Stage
M(%)F(%)IIIIIIIV
HCC139120 (86.33%)19 (13.67%)55.63± 11.22(25-80)514840-
HC203117 (57.64%)86 (42.36%)47.68± 10.78(23-76)----

Results

This assay demonstrated an average sensitivity of 96% and a specificity over 98% in detecting HCC. In our cohort, 47 of 137 HCC patients (35.77%) were AFP negative (AFP<20ng/ml, stage I n = 18, stage II n = 17 and stage III n = 12). Here, we showed that the LDI-TOF-MS recognized almost all AFP-negative HCC. The sensitivity and specificity were obviously superior to AFP in HCC: only 2 of 137 HCCs (1.46%) were misclassified as healthy controls. In contrast, AFP positive and AFP negative HCCs were not readily distinguished by this method. Therefore, this method was independent of tumor markers.

Conclusions

This work established a low-cost, high-throughput procedure based on trace amount of serum to identify HCC as well as healthy controls with superior precision, making it a promising technique for clinical cancer research and translation.

Clinical trial identification

Editorial acknowledgement

Legal entity responsible for the study

Zhongshan Hospital, Fudan University.

Funding

Has not received any funding.

Disclosure

All authors have declared no conflicts of interest.

This site uses cookies. Some of these cookies are essential, while others help us improve your experience by providing insights into how the site is being used.

For more detailed information on the cookies we use, please check our Privacy Policy.

Customise settings
  • Necessary cookies enable core functionality. The website cannot function properly without these cookies, and you can only disable them by changing your browser preferences.