Enabling Circulating Cell-free mRNA Profiling to Empower Cancer Early Detection

Chen-Hsiung Y^*
*Correspondence: Chen-Hsiung Y, Circulogene Theranostics, 3125 Independence Drive, Suite 301, Birmingham, AL, 35209, USA, Email:

Keywords

Gene expression • Pancreatic cancer • Biomarkers • Protein-coding mRNAs

Introduction

The emergence of relatively non-invasive liquid biopsy as a complementary approach to surgical biopsies has fueled intensive research effort and investment. Circulating cell-free nucleic acids in this respect have revolutionized cancer diagnosis in recent decade, allowing non-invasive, real-time and longitudinal interrogation for genomic alterations using a single sample of blood [1]. Consequently, the use of cell-free nucleic acids as biomarkers could facilitate the early detection of diseases such as cancer and enable simple, specific monitoring of disease progression.

In addition to DNAs, protein-coding mRNAs from the tumor tissues are released into the blood, enriched over time and can reflect changes in tumorspecific gene expression. Plasma cfDNA methylation and mutation events are less dynamic and likely provide limited information on tissue homeostasis and disruption. In contrast, circulating cell-free mRNA (cell free m-RNA) profiling could provide richer molecular content compared to other non-invasive biomarkers and constitutes a unique non-invasive interrogation of tissue function in scenarios such as early detection of disease, early drug engagement and response in patients. Combined with advances in molecular diagnostics, systematic profiling of cell free m-RNA can improve our understanding of cancer pathology and identify novel biomarkers for early detection, without the need for invasive biopsy. The potential clinical utility of cell free m-RNA has been demonstrated in patients with various malignant cancers [2-4].

Understanding the mechanisms underlying the presence of mRNA transcripts in circulation is essential to interpret their clinical value.

Methodology

Circulating cell free m-RNA abundance can be influenced by physiological state, level of nucleases in the blood, the half-life of individual cell free m-RNA and the clearance rate by immune system, liver and kidney [2]. Certain cell free m-RNA species are in complexed forms that protects them from degradation by RNases. This ensures their unique stability in the circulation, in contrast to complex-free RNA, which is rapidly degraded [5]. Therefore, key challenges in the cell free m-RNA testing include its extremely low abundance, susceptible to degradation, relatively unstable and poor extraction efficiency. To circumvent these limitations, a signal amplification step following cell free m-RNA extractions should be performed as we discovered in our studies. Circulating cell free m-RNA carries information from human tissues; the pattern of cell free m-RNA expression reflects dysregulation of cancer immunity, tumor cell growth, proliferation and stromal interaction, which makes cell free m-RNA expression signature a promising biomarker for early diagnostic, prognostic and therapeutic purposes [6,7].

Circulating cell free m-RNA in plasma is usually made up of degraded small fragments with size smaller than 200 nucleotides, very low concentration (average lower than 10 ng/mL), and with different terminal modification, these properties make it difficult to investigate [8-10]. The current molecular techniques employed for the detection and characterization of cell free m-RNA include microarrays, RT-qPCR and next-generation sequencing (NGS; RNASeq) [11-14]. Microarrays had been widely used to define circulating microRNA expression. However, due to their limited sensitivity microarrays can only screen the most abundant RNA in biofluids. On the contrary, both RT-qPCR and NGS can detect low abundant cell free m-RNA and remain currently the methods of choice. NGS has been used for cell free m-RNA studies, but some intrinsic problems were not solved, including labor-intensive, time-consuming, requirement of large volume of blood, no standard sequencing method for all RNA fractions, high cost for large scale RNA library preparation, as well as low mapping rate and thus low sensitivity [15-17]. RT-qPCR is a more convenient, sensitive and cost-effective approach, with pre-loaded custom plates, further enhancing its capability as an automate and high throughput platform.

Developing a simple, highly reliable, cost-efficient and non-invasive diagnostic cell free m-RNA test system to screen and identify early stages without the use of a tissue biopsy would significantly reduce both the mortality and the economic burden associated with cancer.

Tissue biopsy and associated approaches which are highly dependent on skills of an operator and the availability of costly equipment could hardly fit into a model of point-of-care diagnostics. The absence of clear alternatives prompts the development and validation of functionalized gene signatures where each individual gene would, ideally, reflect certain pathophysiological process contributing to specific cancer progression in a given individual and predict its outcome. Although multiple research reports have demonstrated amazing promises of circulating cell free m-RNA for diagnostic application, this field is still in its infancy. It is imperative and of paramount interest to harness highly sensitive cell free m-RNA detection technologies and establishing unique expression signatures as early-stage fingerprints of oncogenesis in biological fluids. The fundamental advantage of circulating cell free m-RNA over protein biomarkers is that, unlike proteins, nucleic acids can be detected by a PCR which has the detection threshold of a single molecule. A cell free m-RNA-based expression signature could, if necessary, be augmented by other blood-based biomarkers including cancer-specific cfDNA. The cell free m-RNA expression profiling can be considered as a compendium of transcripts collected from all organs. Some of these circulating transcripts correspond to “true” tissue-specific or cancer type-specific genes, strongly supporting interrogation of these biomolecules to dynamically monitor early pathological changes of tissues and organs. In contrast to poorly functional annotation of non-coding RNA, the coding cell free m-RNA expression profiling provides direct access to both genetic information and functional information pertaining to the tissue of origin and its physiology. Previous studies have reported transcripts in circulation encoding functional information of the liver, brain, immune system, or fetal development [7,11,12]. Therefore, cell free m-RNA expression pattern has the capability of integrating functional and genetic information of tissues, highlighting this analyte’s unique potential as a noninvasive biomarker.

Our comprehensive cell free m-RNA profiling data here provides circulating transcript snapshots of gene expression signatures in patients with lung, pancreatic or breast cancer. The cell free m-RNA expression signature will allow non-invasive delineation of cancer type, early detection, and progression monitoring. Our data further provide promising proof of concept of using cell free m-RNA profiling to monitor early onset cancer activity, which could lead to improved therapeutic management of cancer patients, and eventually alleviate the need for invasive biopsies.

Conclusion

We have comprehensively explored cell free m-RNA expression profiles and signatures of different cancer types, thereby establishing a plasma-based functional transcriptomic databank, including differential gene expression, classification, functional clustering and cancer type-specific signatures. We believe that our work has research, clinical, and diagnostic value, and provides greater dimensionality to the current landscape of cell free m-RNA research and makes a relevant jump into understanding and devising strategies to tackle early cancer detection.

Future Perspectives

Prior studies have laid a solid foundation for tissue-based gene expression signatures in cancer diagnosis and prognosis. However, gene expression signatures for early cancer detection were hampered by tissue unavailability and limited bioinformatics tools, and in many ways spurred renewed interest in developing non-invasive blood-based high-dimensional analytical algorithms. With our cell free m-RNA gene expression database, it is now possible to derive highly accurate and sensitive cancer type-specific signatures that are amenable to large-scale clinical validation. Our profiling model comprised of 750 cancer-associated genes has the potential to be translated into widely applicable assays. While the clinical validity of cell free m-RNA signatures will ultimately be proven by cross-validation in prospective studies, the accurate extraction of information from genomic and/or epigenomic studies is of vital importance for guiding such studies.

The establishment of cancer type-specific cell free m-RNA expression signatures is necessarily an ongoing and dynamic process, in which, with the inclusion of more early-stage patient samples with consistent clinical information, an early detection signature will be continuously refined. Due to biological and technical limitations, cell free m-RNA-based expression signatures may not be able to achieve 100% accuracy, yet the application of advanced feature selection algorithms and the combination of genetic and clinical data will enable their robust performance with dramatically reduced complexity.

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