Frontiers in Bioengineering and Biotechnology (Apr 2022)

Detection and Localization of Solid Tumors Utilizing the Cancer-Type-Specific Mutational Signatures

  • Ziyu Wang,
  • Ziyu Wang,
  • Ziyu Wang,
  • Tingting Zhang,
  • Tingting Zhang,
  • Tingting Zhang,
  • Wei Wu,
  • Wei Wu,
  • Wei Wu,
  • Lingxiang Wu,
  • Lingxiang Wu,
  • Lingxiang Wu,
  • Jie Li,
  • Jie Li,
  • Jie Li,
  • Bin Huang,
  • Bin Huang,
  • Bin Huang,
  • Yuan Liang,
  • Yuan Liang,
  • Yuan Liang,
  • Yan Li,
  • Yan Li,
  • Yan Li,
  • Pengping Li,
  • Pengping Li,
  • Pengping Li,
  • Kening Li,
  • Kening Li,
  • Kening Li,
  • Wei Wang,
  • Renhua Guo,
  • Qianghu Wang,
  • Qianghu Wang,
  • Qianghu Wang

DOI
https://doi.org/10.3389/fbioe.2022.883791
Journal volume & issue
Vol. 10

Abstract

Read online

Accurate detection and location of tumor lesions are essential for improving the diagnosis and personalized cancer therapy. However, the diagnosis of lesions with fuzzy histology is mainly dependent on experiences and with low accuracy and efficiency. Here, we developed a logistic regression model based on mutational signatures (MS) for each cancer type to trace the tumor origin. We observed MS could distinguish cancer from inflammation and healthy individuals. By collecting extensive datasets of samples from ten tumor types in the training cohort (5,001 samples) and independent testing cohort (2,580 samples), cancer-type-specific MS patterns (CTS-MS) were identified and had a robust performance in distinguishing different types of primary and metastatic solid tumors (AUC:0.76 ∼ 0.93). Moreover, we validated our model in an Asian population and found that the AUC of our model in predicting the tumor origin of the Asian population was higher than 0.7. The metastatic tumor lesions inherited the MS pattern of the primary tumor, suggesting the capability of MS in identifying the tissue-of-origin for metastatic cancers. Furthermore, we distinguished breast cancer and prostate cancer with 90% accuracy by combining somatic mutations and CTS-MS from cfDNA, indicating that the CTS-MS could improve the accuracy of cancer-type prediction by cfDNA. In summary, our study demonstrated that MS was a novel reliable biomarker for diagnosing solid tumors and provided new insights into predicting tissue-of-origin.

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