Comparison of Genetic Profiling between Primary Tumor and Circulating Tumor Cells Captured by Microfluidics in Epithelial Ovarian Cancer: Tumor Heterogeneity or Allele Dropout?
Ting-Yu ChangSheng-Wen ChenWen-Hsiang LinChung-Er HuangMark I EvansI-Fang ChungJanne-Wha WuGwo-Chin MaMing ChenPublished in: Diagnostics (Basel, Switzerland) (2021)
Epithelial ovarian cancer (EOC) is a leading cause of cancer mortality among women but unfortunately is usually not diagnosed until advanced stage. Early detection of EOC is of paramount importance to improve outcomes. Liquid biopsy of circulating tumor cells (CTCs) is emerging as one of the promising biomarkers for early detection of solid tumors. However, discrepancies in terms of oncogenomics (i.e., different genetic defects detected) between the germline, primary tumor, and liquid biopsy are a serious concern and may adversely affect downstream cancer management. Here, we illustrate the potential and pitfalls of CTCs by presenting two patients of Stage I EOC. We successfully isolated and recovered CTCs by a silicon-based nanostructured microfluidics system, the automated Cell RevealTM. We examined the genomics of CTCs as well as the primary tumor and germline control (peripheral blood mononuclear cells) by whole exome sequencing. Different signatures were then investigated by comparisons of identified mutation loci distinguishing those that may only arise in the primary tumor or CTCs. A novel model is proposed to test if the highly variable allele frequencies, between primary tumor and CTCs results, are due to allele dropout in plural CTCs or tumor heterogeneity. This proof-of-principle study provides a strategy to elucidate the possible cause of genomic discrepancy between the germline, primary tumor, and CTCs, which is helpful for further large-scale use of such technology to be integrated into clinical management protocols.
Keyphrases
- circulating tumor cells
- circulating tumor
- single cell
- machine learning
- squamous cell carcinoma
- pregnant women
- type diabetes
- high throughput
- coronary artery disease
- dna repair
- cardiovascular disease
- papillary thyroid
- deep learning
- newly diagnosed
- cell therapy
- lymph node metastasis
- fine needle aspiration
- patient reported outcomes
- cardiovascular events
- atomic force microscopy