Plasma cell-free RNA profiling distinguishes cancers from pre-malignant conditions in solid and hematologic malignancies.
Breeshey Roskams-HieterHyun Ji KimPavana AnurJosiah T WagnerRowan CallahanElias SpiliotopoulosCharles Ward KirschbaumFehmi CivitciPaul T SpellmanReid F ThompsonKhashayar FarsadWillscott E NauglerThuy T M NgoPublished in: NPJ precision oncology (2022)
Cell-free RNA (cfRNA) in plasma reflects phenotypic alterations of both localized sites of cancer and the systemic host response. Here we report that cfRNA sequencing enables the discovery of messenger RNA (mRNA) biomarkers in plasma with the tissue of origin-specific to cancer types and precancerous conditions in both solid and hematologic malignancies. To explore the diagnostic potential of total cfRNA from blood, we sequenced plasma samples of eight hepatocellular carcinoma (HCC) and ten multiple myeloma (MM) patients, 12 patients of their respective precancerous conditions, and 20 non-cancer (NC) donors. We identified distinct gene sets and built classification models using Random Forest and linear discriminant analysis algorithms that could distinguish cancer patients from premalignant conditions and NC individuals with high accuracy. Plasma cfRNA biomarkers of HCC are liver-specific genes and biomarkers of MM are highly expressed in the bone marrow compared to other tissues and are related to cell cycle processes. The cfRNA level of these biomarkers displayed a gradual transition from noncancerous states through precancerous conditions and cancer. Sequencing data were cross-validated by quantitative reverse transcription PCR and cfRNA biomarkers were validated in an independent sample set (20 HCC, 9 MM, and 10 NC) with AUC greater than 0.86. cfRNA results observed in precancerous conditions require further validation. This work demonstrates a proof of principle for using mRNA transcripts in plasma with a small panel of genes to distinguish between cancers, noncancerous states, and precancerous conditions.
Keyphrases
- cell free
- papillary thyroid
- end stage renal disease
- cell cycle
- squamous cell
- ejection fraction
- bone marrow
- chronic kidney disease
- machine learning
- genome wide
- gene expression
- childhood cancer
- lymph node metastasis
- prognostic factors
- peritoneal dialysis
- deep learning
- cell proliferation
- climate change
- young adults
- high throughput
- circulating tumor
- risk assessment
- patient reported