BMC Cancer (Mar 2023)

Fragment length profiles of cancer mutations enhance detection of circulating tumor DNA in patients with early-stage hepatocellular carcinoma

  • Van-Chu Nguyen,
  • Trong Hieu Nguyen,
  • Thanh Hai Phan,
  • Thanh-Huong Thi Tran,
  • Thu Thuy Thi Pham,
  • Tan Dat Ho,
  • Hue Hanh Thi Nguyen,
  • Minh-Long Duong,
  • Cao Minh Nguyen,
  • Que-Tran Bui Nguyen,
  • Hoai-Phuong Thi Bach,
  • Van-Vu Kim,
  • The-Anh Pham,
  • Bao Toan Nguyen,
  • Thanh Nhan Vo Nguyen,
  • Le Anh Khoa Huynh,
  • Vu Uyen Tran,
  • Thuy Thi Thu Tran,
  • Thanh Dang Nguyen,
  • Dung Thai Bieu Phu,
  • Boi Hoan Huu Phan,
  • Quynh-Tho Thi Nguyen,
  • Dinh-Kiet Truong,
  • Thanh-Thuy Thi Do,
  • Hoai-Nghia Nguyen,
  • Minh-Duy Phan,
  • Hoa Giang,
  • Le Son Tran

DOI
https://doi.org/10.1186/s12885-023-10681-0
Journal volume & issue
Vol. 23, no. 1
pp. 1 – 17

Abstract

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Abstract Background Late detection of hepatocellular carcinoma (HCC) results in an overall 5-year survival rate of less than 16%. Liquid biopsy (LB) assays based on detecting circulating tumor DNA (ctDNA) might provide an opportunity to detect HCC early noninvasively. Increasing evidence indicates that ctDNA detection using mutation-based assays is significantly challenged by the abundance of white blood cell-derived mutations, non-tumor tissue-derived somatic mutations in plasma, and the mutational tumor heterogeneity. Methods Here, we employed concurrent analysis of cancer-related mutations, and their fragment length profiles to differentiate mutations from different sources. To distinguish persons with HCC (PwHCC) from healthy participants, we built a classification model using three fragmentomic features of ctDNA through deep sequencing of thirteen genes associated with HCC. Results Our model achieved an area under the curve (AUC) of 0.88, a sensitivity of 89%, and a specificity of 82% in the discovery cohort consisting of 55 PwHCC and 55 healthy participants. In an independent validation cohort of 54 PwHCC and 53 healthy participants, the established model achieved comparable classification performance with an AUC of 0.86 and yielded a sensitivity and specificity of 81%. Conclusions Our study provides a rationale for subsequent clinical evaluation of our assay performance in a large-scale prospective study.

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