Transcriptomic profiling of plasma extracellular vesicles enables reliable annotation of the cancer-specific transcriptome and molecular subtype

Keeping track of disease progression and treatment response is crucial for effective patient management. Traditionally, this has often required invasive procedures and tissue sampling. However, recent advancements in liquid biopsy techniques have opened up new avenues for non-invasive monitoring, offering a wealth of information from simple blood samples.

A recent study has highlighted the potential of plasma RNA analysis, particularly focusing on extracellular vesicles (EVs) that carry RNA molecules (evRNA). These tiny vesicles, released by cancer cells into the bloodstream, contain valuable genetic information that can provide insights into tumor characteristics and response to treatment.

Utilizing cutting-edge deep learning algorithms, researchers at the University of Texas MD Anderson Cancer Center have been able to analyze the transcriptomes of evRNA and predict consensus molecular subtypes in patients with metastatic colorectal cancer. This groundbreaking approach allows for the identification of specific molecular profiles associated with different subtypes of the disease, providing valuable information for personalized treatment strategies.

Performance of CMS subtyping of liquid biopsies from patients with mCRC

a, A visual overview of workflow. RNA-seq was performed on tumor samples and plasma EV of cancer patients as well as plasma EV of healthy controls. For deconvolution with CIBERSORTx and CODEFACS, a signature matrix was created using genes that are enriched in the tumor samples and in the plasma EV of healthy controls. Deconvolution could impute the proportion of cancer present in bulk plasma evRNA, which also allowed for generation of ROC curve based on whether cancer RNA was present in the sample. Deconvolved plasma EV profiles were also used in gene set enrichment analysis (GSEA) and artificial neural network to assign the CMS groups, which were then compared to the subtypes of the tumor samples. b, Cohorts of CRC patients analyzed in this study. In the baseline cohort, molecular subtypes of tumor tissues and their matched plasma evRNA samples were compared. In the longitudinal cohort, molecular subtype switch and emerging changes at the gene and pathway level were evaluated and compared, at each serial point, in patients with and without recurrence.

Moreover, the study demonstrates the dynamic nature of tumor subtypes under treatment pressure, showcasing the potential of evRNA analysis in monitoring changes over time. By tracking transcriptomic alterations, researchers can gain insights into the effectiveness of treatment and identify potential resistance mechanisms.

One of the most exciting findings of the study is the ability to detect expressed gene fusions and neoepitopes from evRNA. Gene fusions and neoepitopes are unique genetic signatures of cancer cells that can serve as targets for precision therapies. The ability to identify these targets from liquid biopsies opens up new possibilities for personalized cancer treatment without the need for invasive tissue sampling.

Overall, this study underscores the immense potential of transcriptomic-based liquid biopsy platforms in the field of precision oncology. By harnessing the power of evRNA analysis, researchers can not only monitor disease progression but also identify actionable targets for therapy. This non-invasive approach has the potential to revolutionize cancer care, offering patients a safer and more effective means of diagnosis and treatment monitoring. As research in this field continues to advance, we can expect even greater insights into the complexities of cancer biology and more targeted therapies tailored to individual patients.

Bahrambeigi V, Lee JJ, Branchi V, Rajapakshe KI, Xu Z, Kui N, Henry JT, Wang K, Stephens BM, Dhebat S, Hurd MW, Sun R, Yang P, Ruppin E, Wang W, Kopetz S, Maitra A, Guerrero PA. (2024) Transcriptomic Profiling of Plasma Extracellular Vesicles Enables Reliable Annotation of the Cancer-specific Transcriptome and Molecular Subtype. Cancer Res [Epub ahead of print]. [abstract]

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