An Analysis of Transcriptomic Burden Identifies Biological Progression Roadmaps for Hematological Malignancies and Solid Tumors

Transcriptomic burden (TcB) refers to the cumulative effect of alterations in the expression levels of genes within a cell or tissue. It is typically assessed by analyzing gene expression profiles, which involve measuring the levels of messenger RNA transcripts produced by genes within a sample of cells or tissue. This can be done using techniques such as microarray analysis, RNA sequencing, or quantitative polymerase chain reaction. TcB can arise from genetic mutations, epigenetic changes, environmental factors, and other factors that affect gene expression. TcB imbalances ultimately lead to changes in cellular functions, disease development, and/or response to therapy. For example, TcB has been implicated in cancer, where alterations in gene expression can promote cancer growth and the development of resistance to chemotherapy, which can reduce drug effectiveness.

While TcB analysis has been an invaluable tool for understanding disease development and progression, there are several limitations and challenges that must be addressed. Firstly, current TcB analysis is limited by the quality and availability of samples. Obtaining high-quality samples that accurately represent the cellular or tissue type of interest can be challenging, particularly in cases where the tissue of interest is difficult to access or is not easily sampled. Additionally, sample quality can be affected by factors such as sample storage conditions, sample processing, and RNA extraction protocols, all of which can introduce bias and affect the accuracy of gene expression measurements. Another challenge is the complex nature of transcriptomic data analysis. Transcriptomic data analysis involves complex bioinformatics pipelines that require specialized skills and computational resources. Analysis of large datasets can be time-consuming and computationally intensive, requiring significant expertise in data management, statistical analysis, and programming. Furthermore, TcB analysis can be influenced by the heterogeneity of cellular populations within a tumor. Different cells within a tumor may exhibit different gene expression profiles, which can confound analyses and make it challenging to identify changes in gene expression that are specifically associated with cancer progression. Another limitation of current TcB analysis is that it typically provides a snapshot of gene expression at a single time point. However, gene expression profiles can change over time, and changes in gene expression may be dynamic, occurring at different stages of disease development or in response to therapy. Finally, TcB analysis can be limited by the lack of functional validation of identified differentially expressed genes. The identification of differentially expressed genes does not necessarily provide information about their biological significance, and additional experiments, such as functional assays or pathway analyses, may be required to validate the functional relevance of differentially expressed genes. Despite technological and computational advances, there is a need for novel methods to analyze tumor transcriptomes and identify the biological progression roadmaps for different types of cancer.

In a new study published in the peer-reviewed journal Biomedicines, Dr. Dashnamoorthy Ravi, Dr. Afshin Beheshti, Dr. Kristine Burgess, Dr. Athena Kritharis, Dr. Ying Chen, Dr. Andrew Evens and led by Associate Professor Biju Parekkadan from Rutgers University, developed a new method to analyze RNA sequencing data that integrates individual TcB and integrated gene behavior function using a computational set of algorithms. The authors thus created an analysis tool that first stratifies a patient’s TcB and then evaluates differential gene behavior within TcB clusters and gained tremendous insight by studying ~ 5000 patients across a wide variety of cancers.

The research team approach to studying cancer learned that as tumors progress, the complexity of the genetic information they produce (transcriptional complexity) will continue to increase. This results in an increased burden of information, which can be measured by TcB. The amount of DNA in a cell remains relatively constant and can be quickly synthesized, but RNA levels are higher and require more time to produce enough for cell division. When the constraint of time to produce RNA is removed, malignant cells can proliferate more rapidly, leading to an increase in their RNA content over time. The authors provided ample of evidence to support their idea, including a shift from low to high TcB in Acute Lymphoblastic Leukemia (ALL), reversibility of TcB in canine lymphoma with a BKM120 (a PI3K inhibitor therapy), and a transcriptional lag in SUDHL4 cells treated with BKM120. They used TcB to analyze tumor transcriptomes, revealing progressive biological features that provide a foundation for more comprehensive analyses. Their findings suggest that there is a homogeneity in tumors in terms of higher-order biological processes that promote malignant cell proliferation, while the signaling pathways involved in promoting such growth show heterogeneity.

The identification of the central dogmatic principles of translation (ribogenesis), replication, and transcription, which intersect with the cell cycle, defines the biological mechanisms of tumor progression. The results also support the theory of embryonic reversal-based definition of malignant progression, as changes observed in translation (ribogenesis), replication, transcription, and the extracellular matrix in primary human tumors followed the opposite directions of higher-order processes occurring in human embryonic development. Investigators found that as TcB increases, biological networks that include signaling pathways, ribosomal biogenesis, translation, transcription, and the cell cycle remain interconnected across low and mid TcB in Diffuse Large B-Cell Lymphoma (DLBCL) and ALL. Furthermore, combining transcriptomic data with mutation profiles in bladder cancer, authors identified predictable evolutionary patterns. The number of genes mutated at low TcB dramatically increased from mid to high TcB, indicating tumor progression.

In summary, the authors findings demonstrate that the new powerful TcB methodology can be used to gain insights into the underlying mechanisms of cancer and develop new strategies for diagnosis, treatment, and prevention. The study has important implications for personalized cancer treatment and highlights the importance of understanding the complex interplay between transcriptional complexity, TcB, and cancer progression.