The Application of RNA-Seq and Proteomics in Cancer Research
Published on June 20th, 2023
⏱ 5 min read
by BigOmics
Introduction
Pharmaceutical companies are under intense pressure to succeed in the search for new targets, biomarkers and therapeutics: biology is complex, and the sheer volume of data generated by omics technologies makes its analysis anything but simple. Fortunately, more sophisticated tools are also being developed to help us overcome these challenges and gain new insights. Two of these are RNA-Seq and proteomics. In this article, we take a closer look at how RNA-Seq and proteomics can revolutionise the way pharmaceutical companies approach drug discovery and cancer treatment . These innovative tools allow us to take a completely new approach.
RNA Sequencing: Illuminating Gene Expression Patterns to Identify Novel Therapeutic Targets in Cancer
RNA-Seq allows researchers to measure the expression levels of thousands of genes simultaneously. It provides a comprehensive view of gene expression patterns in different tissues, cells, or conditions, enabling researchers to identify potential therapeutic targets. By analyzing RNA-Seq data, pharma companies can gain insights into the underlying molecular mechanisms of cancer, helping them develop more effective treatments. The contributions of RNA-Seq to cancer research include, among others, studies on differential gene expression analysis and cancer biomarkers, cancer heterogeneity and evolution, anticancer drug resistance, cancer microenvironment and immunotherapy, and neoantigens [Hong et al.].
Cancer is caused by genetic alterations, and the availability of massively parallel sequencing has enabled systematic documentation of these alterations at the whole-genome level. Comprehensive studies include the integrative analysis of 2,658 cancer genomes and their associated normal tissues from 38 tumor types. [ICGC/TCGA]
Proteomics in Cancer Treatment
Proteomics plays a central role in cancer drug discovery. It provides insights into the totality of proteins and corresponding gene expression levels in a cell, giving a comprehensive understanding of the molecular mechanisms underlying cancer development and progression.
By integrating proteomic and transcriptomic data, researchers can find potential biomarkers, discover new targets, and develop more effective cancer treatments tailored to individual patients.
Transcriptomics and proteomics together enable a comprehensive analysis of gene expression and protein abundance in cancer cells and tissues. By comparing cancer samples to healthy tissues, researchers can identify genes and proteins that are differentially expressed in cancer. These differentially expressed molecules represent potential therapeutic targets. Pharmaceutical companies can then develop drugs or therapies that specifically target these molecules to disrupt cancer cell growth and survival.
Transcriptomics and Proteomics in Personalized Medicine: Unlocking the Potential of Molecular Subtypes
Transcriptomics and proteomics have revolutionized personalized medicine by stratifying patients into distinct molecular subtypes based on gene expression profiles or protein signatures. This molecular profiling identifies individuals who respond to specific therapies and have a higher risk of disease recurrence.
It has become apparent that the old classification of tumors based on tissue of origin was inadequate and led to a one-size-fits-all therapeutic approach. We now know that genomic characteristics are more accurate for categorizing tumor types. [O’Neill et al.]
Pharmaceutical companies can develop targeted therapies tailored to these molecular subtypes, leading to more effective treatments. In the context of cancer research, RNA-Seq has proven invaluable in profiling gene expression in patient-derived tumor samples, enabling the identification of different molecular subtypes within each cancer type.
These subtypes, considered separate diseases originating from the same tissue, hold great potential for developing targeted therapeutics. However, understanding the underlying mechanisms driving these subtypes can be challenging due to lineage-specific expression patterns. [Martínez et al.]
Unveiling and Decoding the Hidden Clues: Biomarker Discovery and Drug Repurposing
Transcriptomics and proteomics can help identify biomarkers, which are molecular indicators of disease presence, progression, or treatment response. These biomarkers can be used for early detection, accurate diagnosis, and monitoring of treatment response in cancer patients. Pharma companies can develop diagnostic tests or companion diagnostics that utilize these biomarkers to guide treatment decisions and improve patient outcomes. By analyzing changes in gene expression or protein expression profiles upon drug treatment, researchers can uncover the pathways and cellular processes affected by the drug. This knowledge can help optimize treatment strategies, identify potential drug combinations, understand the mechanisms of drug resistance, and figure out how to overcome it with appropriate drug combinations – or at least delay its onset by selecting appropriate combinations. [Bayat Mokhtari et al.]
By understanding the importance of drug repurposing in connection with combination therapies, researchers can select drug combinations based on how one drug makes a tumor more susceptible to its partner drug [Ayoub et al.] – questions that can actually be well explored with the BigOmics platform.
Transcriptomics and proteomics data can also provide insights into the effects of existing drugs on cancer-related genes or proteins. Pharma companies can leverage this information to repurpose approved drugs for cancer treatment. By finding drugs that target specific dysregulated pathways or interact with relevant proteins, pharma companies can significantly cut the time and costs of new drug discovery.
Proteomics allows researchers to identify and measure thousands of proteins in a biological sample and gain a functional overview of biological systems. It has a wide range of applications: The technique can help identify potential therapeutic targets, biomarkers [Kwon et al.], and markers of drug response. By analyzing proteomics data, pharmaceutical companies gain insights into the complex interplay of proteins in cells and tissues and ultimately a deeper understanding of disease mechanisms.
Proteomics enables the study of protein-protein interactions, post-translational modifications, and subcellular localization. These aspects provide valuable information about the intricate regulatory networks and signaling pathways involved in disease progression. By deciphering these molecular interactions, pharmaceutical companies can identify targets that may have been previously overlooked, opening new opportunities for therapeutic intervention.
Harnessing the Power of RNA-Seq and Proteomics in the Oncology Field
Integrating RNA-Seq and proteomics data can provide even more insights for cancer drug discovery and development. When combined, these techniques bring a more holistic understanding of disease biology: potential drug targets can then be identified with greater confidence. Integrated analysis of RNA-Seq and proteomics data can also reveal critical genes and proteins that drive disease progression, enabling the development of more targeted and effective therapies.
Ultimately, a comprehensive understanding of disease biology is obtained that can lead to the discovery of potential therapeutic targets, the development of personalized treatment strategies, and the optimization of drug repurposing efforts.
It is also worth noticing that leads that appear solid in RNA-Seq studies may be disqualified in proteomic analysis (e.g., due to post-translational modifications). Thus, it is not only a matter of gaining more confidence, but also of avoiding “red herrings”.
The BigOmics platform, tackles these challenges head-on by offering a comprehensive workflow tailored specifically for analyzing RNA-Seq and proteomics data. Our goal is to create smart tools that empower individuals to delve into advanced omics analysis. We streamline the integration of datasets, unveiling crucial genes and proteins involved in cancer biology. By supporting drug repurposing and unraveling the intricate mechanisms of novel compounds, we contribute to a deeper comprehension of disease mechanisms.
Want to learn more about how RNA-Seq and proteomics provide invaluable insights for cancer research? Curious about how bioinformatics data discovery software can simplify the exploration of your RNA-Seq and proteomics data?
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References
Ayoub, Nehad M. “Editorial: Novel Combination Therapies for the Treatment of Solid Cancers.” Frontiers in oncology vol. 11 708943. 18 Jun. 2021, http://doi.org/10.3389/fonc.2021.708943
Bayat Mokhtari, Reza et al. “Combination therapy in combating cancer.” Oncotarget vol. 8,23 (2017): 38022-38043. http://doi.org/10.18632/oncotarget.16723
Hong M, Tao S, Zhang L, et al. RNA sequencing: new technologies and applications in cancer research. J Hematol Oncol. 2020;13(1):166. Published 2020 Dec 4. http://doi.org/10.1186/s13045-020-01005-x
ICGC/TCGA Pan-Cancer Analysis of Whole Genomes Consortium (2020). Pan-cancer analysis of whole genomes. Nature, 578(7793), 82–93. https://doi.org/10.1038/s41586-020-1969-6
Kwon, Y. W., Jo, H. S., Bae, S., Seo, Y., Song, P., Song, M., & Yoon, J. H. (2021). Application of Proteomics in Cancer: Recent Trends and Approaches for Biomarkers Discovery. Frontiers in medicine, 8, 747333. https://doi.org/10.3389/fmed.2021.747333
Martínez, E et al. “Comparison of gene expression patterns across 12 tumor types identifies a cancer supercluster characterized by TP53 mutations and cell cycle defects.” Oncogene vol. 34,21 (2015): 2732-40. http://doi.org/10.1038/onc.2014.216
O’Neill, Ailbhe C et al. “Evolving Cancer Classification in the Era of Personalized Medicine: A Primer for Radiologists.” Korean journal of radiology vol. 18,1 (2017): 6-17. http://doi.org/10.3348/kjr.2017.18.1.6