Abstract PR013: Transcriptomic profiling of liquid biopsy in colorectal cancer

Abstract Objective: Consensus molecular subtype (CMS) is a predictive factor for treatment outcomes of chemotherapies for metastatic colorectal cancer (CRC). CMS classification is based on transcriptomic profiles of CRC specimens obtained by tumor biopsy. Tumor biopsy often provides limited information due to the heterogeneity within tumor and spatial heterogeneity between the primary tumor and distant metastases. In addition, repeated tissue biopsy to monitor the treatment response is not practical. Liquid biopsy is a minimally invasive method for the real-time monitoring of cancer-derived biomarkers. Among liquid biopsy biomarkers, extracellular vesicles (EVs) have a unique potential because they possess nucleic acids. We explored the use of CRC plasma EVs in predicting the molecular subtype of CRCs using RNA-seq. Methods: EVs were isolated from 10 different CMS-stratified colorectal cancer cell lines and a normal control. Whole transcriptome RNA-seq on cellular RNA (cRNA) and cell-derived evRNA was done to determine if EVs could be used to predict the CMS subtype of their cells of origin. We then sought to perform molecular subtyping of plasma EVs from patients with CRC. RNA-seq was performed on tumor tissues and matched plasma EVs from 46 patients with CRC and 59 healthy controls (age/gender matched). The bulk transcriptome from CRC plasma EVs was deconvoluted to predict the cancer percentage (CIBERSORTx) and to utilize cancer-specific transcriptome for CMS subtyping (DeMixT). Artificial neural network (ANN)-based CMS subtype classifier was used to classify molecular subtypes. Results: There was 100% concordance between CMS subtype of evRNA with that of the cRNA. We showed that, RNA mixtures containing as low as 1% cancer cell RNA could be accurately classified into the correct CMS class. Imputed proportions of cancer in plasma of CRC patients ranged from 0.89% to 2.08%. Receiver operating characteristic (ROC) curve showed area under the curve of 0.961 with specificity and sensitivity of 0.96 and 0.9, respectively. Plasma evRNA was classified into CMS classes and there was 67% (31/46) concordance between the predicted subtype of liquid biopsies and the tumor samples. In patients with tumor purity greater than 10%, the concordance was higher at 75% (27/36). Conclusions: EVs could be used to accurately predict CMS subtypes of their cells of origin. We created a pipeline using low-input RNA library preparation from plasma EV to estimate the cancer RNA portion present in the bulk transcriptome and predict the molecular subtypes of colorectal cancers. Molecular subtyping of evRNA may help to track CMS changes of the tumor in patients undergoing treatment. Citation Format: Vahid Bahrambeigi, Jaewon J. Lee, Kimal I. Rajapakshe, Bret M. Stephens, Jason T. Henry, Sarah Dhebat, Mark W. Hurd, Ryan Sun, Scott Kopetz, Anirban Maitra, Paola A. Guerrero. Transcriptomic profiling of liquid biopsy in colorectal cancer [abstract]. In: Proceedings of the AACR Special Conference on Colorectal Cancer; 2022 Oct 1-4; Portland, OR. Philadelphia (PA): AACR; Cancer Res 2022;82(23 Suppl_1):Abstract nr PR013.