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  • How RNA-Seq is Transforming Cancer Studies

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    Cancer research has been transformed through numerous molecular techniques, with RNA sequencing (RNA-seq) playing a crucial role in understanding the complexity of the disease. Maša Ivin, Ph.D., Scientific Writer at Lexogen, and Yvonne Goepel Ph.D., Product Manager at Lexogen, remarked that “The high-throughput nature of RNA-seq allows for rapid profiling and deep exploration of the transcriptome.” They emphasized its indispensable role in cancer research, aiding in biomarker discovery, characterization of cancer heterogeneity and evolution, understanding drug resistance, and investigating aspects like the cancer immune microenvironment, immunotherapy, neoantigens, and more.

    Clearly, RNA-seq is just one of the many technologies propelling cancer research forward. Staple methods such as RT-qPCR, microarrays, and spatial imaging, among others, continue to provide insights into the disease. Additionally, DNA sequencing is vital for identifying and comprehending the mutations at the root of these cancers. However, exploring cancer with RNA-seq provides a deeper layer of understanding of the changes within these cells. For instance, if we consider a cell as a computer, the DNA can be compared to the computer's stored memory, while the RNA resembles the active processes or the running applications. Taking a closer look at these processes offers a real-time glimpse into the computer's operations, or, in this context, the ongoing activities within cancer cells.

    Applications of RNA-seq in Cancer Research
    Cancer begins with mutations in DNA that disrupt normal cell growth and regulation, leading to uncontrolled proliferation. “RNA-seq offers a path to studying the consequences of those genomic alterations and deepens our understanding of cancer in all its complexity,” explained Ivin and Goepel. “Cancer cells are known to be highly susceptible to accumulating genomic mutations. Because RNA-seq specifically interrogates only the expressed transcripts, it allows us to identify the mutations and alterations that drive cancer progression or resistance to treatment—sorting out the needles in the haystack.”

    The Lexogen team laid out a number of ways that RNA-seq has contributed to our understanding of cancer. The first involves identifying the functions and cellular processes at the molecular level of genes uniquely expressed in cancer cells, along with their regulatory pathways. “Differential gene expression analysis is by far the most common application of RNA-seq and, naturally, plays a major role in cancer research as it helps to reveal the function and molecular mechanisms of genes differentially expressed in various cancers (cells, tumors), degrees of progression, after treatment, etc.,” they noted. “Both 3' mRNA-seq and whole transcriptome RNA-seq are commonly used for transcriptional profiling in cancer research.”

    RNA-seq can also be used in the discovery of cancer biomarkers, in particular those that point to cancer progression or recurrence and biomarkers predictive for treatment responses1,2. Gene fusions, closely linked to oncogenesis, are promising targets for personalized cancer therapies, especially in leukemia, breast, and colorectal cancers. Researchers are also using less studied transcripts, like non-coding RNAs for improved cancer detection3.

    Single-cell RNA-seq (scRNA-seq) is another important application that has become a cornerstone for cancer research. Offering full transcriptome resolution within individual tumor cells, scRNA-seq can reveal the vast cellular variations. While it’s currently limited by the need for intact cell isolation, alternative methods like single-nucleus RNA-seq (snRNA-seq) enable detailed analysis of difficult-to-isolate or preserved cells, further enhancing our grasp of cancer heterogeneity.

    In addition to aiding in the study of the tumor microenvironment, scRNA-seq has allowed for a deeper investigation into immune cell heterogeneity that resolves immune and cancer cell interactions and highlights potential avenues for novel immunotherapies2,4. Most notably, Ivin and Goepel detailed how scRNA-seq has helped identify transcriptional signatures of T cell exhaustion2, as well as increase our understanding of patient responses to immunotherapy ultimately pointing toward combination treatment strategies2,4.

    RNA-seq also plays a vital role in investigating cancer neoantigens and decoding drug resistance. Neoantigens, derived from cancer-specific mutations or gene fusions and recognized as "non-self" by the immune system, are pivotal in the response to cancer immunotherapy and serve as targets for personalized treatments. Cutting-edge sequencing and analytical tools have enabled their rapid identification, paving the way for personalized cancer vaccines and adoptive cell therapy5. Additionally, Ivin and Goepel pointed to the collaborative initiative, the Human Tumor Atlas Network, which has allowed for the creation of 3-dimensional cancer transformation atlases using scRNA-seq data and other comprehensive datasets. These atlases offer deeper insights into cancer heterogeneity and treatment resistance across patients and timeframes than genomic data alone.

    Breakthroughs with RNA-seq
    The number of exciting studies in cancer research enabled by RNA-seq would be too large to fit into a single article. Instead, we’ll cover several chosen by Ivin and Goepel with the opportunity for our community to share some of their favorites in the comments.
    • Zhang et al. studied histone deacetylases (HDACs) as potential anti-cancer targets and found a genetic interaction between HDAC1 and HDAC2 due to common chromosomal deletions in several cancers6. Using RNA technologies such as SLAMseq and QuantSeq, they also analyzed the direct transcriptional effects of HDAC2 degradation.
    • A study by Muhar et al., revealed distinct roles for transcriptional factors MYC and BRD4 in cancer, with BRD4 being a broad co-activator and MYC selectively controlling metabolic processes7. The research offers a scalable approach to pinpoint direct transcriptional targets, vital for cancer studies.
    • Small-molecule drugs, traditionally designed to inhibit target proteins, fail to address about 80% of proteins termed "undruggable." Georg Winter's team at CeMM has innovatively approached this challenge by developing "degraders," specifically "molecular glues," to destabilize these proteins, using RNA-seq to study the targeted degradation effects on potential cancer targets8.
    • In this work, Levy et al., developed a new pipeline to analyze cancer immunopeptidome and identify recurrent post-translational spliced peptides (PTSPs), potential immunotherapy targets, from various cancers. Their method utilizes mass spectrometry data and RNA-seq on melanoma cell lines, thoroughly filtering out alternatives and mostly wild-type peptides using RNA-seq data to ensure the accuracy of identified PTSPs9.
    • Patients with high-risk bladder cancer often relapse after standard BCG therapy, with the resistance mechanisms often being unclear. Rouanne et al. utilized RNA-seq on FFPE tumor RNA to explore immune subversion patterns upon BCG relapse10.
    Emerging Trends and Future Prospects
    Single-cell sequencing is already prevalent in cancer research, but recent advancements have expanded its applications and increased its usage. This technique is now being applied to study important cell populations such as circulating tumor cells (CTCs). These cells detach from primary tumors, circulating in the bloodstream and potentially causing metastasis. “Investigation of CTCs can not only assess the state of metastasis formation strongly influencing the treatment of the patient, but also has tremendous potential for the discovery of targets against cancer metastasis,” noted the Lexogen team.

    The difficulty with CTCs is that they are rare and often become masked by the abundance of blood cells. To overcome these limitations, Ivin and Goepel explained that CTC enrichment techniques have been combined with high-definition single-cell sequencing tools, such as their LUTHOR technology. This allows for a more in-depth analysis of a smaller number of cells to understand the complete transcriptomic profile of the CTCs. Utilizing this type of approach provides the opportunity to subclassify CTCs and differentiate their origin even between tumors at earlier stages11,12,13.

    Researchers are now integrating scRNA-seq with tools like spatial transcriptomics by mapping transcriptional activities within tissues. Spatial transcriptomics conveys the physical layout, which is crucial when analyzing hard-to-extract cells or understanding cell distribution. However, it's limited in discovering new transcripts due to sequence-specific probe design. A refined approach combines laser microdissection with RNA-seq, maintaining spatial data. As emphasized by Lexogen’s team, using advanced RNA-seq methods with laser-microdissections significantly improves sensitivity, even detecting rare transcripts from small samples.

    Furthermore, AI and machine learning are also aiding in cancer research and drug development efforts. Paired with high-throughput shallow RNA-seq, these tools enable large-scale analysis of patient cells, identifying patterns in gene expression that predict cancer development and treatment responses. Then the findings can be used with other patients with similar patterns to speed up diagnosis and improve the treatment outcomes for a better overall prognosis14.

    “Cancer research has come a long way, fueled by amazing high-throughput technologies, including RNA sequencing,” stated the Lexogen team. “It is becoming increasingly clear that the road to even more successful therapies is focusing on personalization when designing therapies, as inter-individual differences in cancer development, progression, and metastasis are evident. Technological advancement needs to follow the needs of the research field, and we believe that should be the case for RNA-seq too.”


    1. Ergin S, Kherad N, Alagoz M. RNA sequencing and its applications in cancer and rare diseases. Molecular Biology Reports. 2022;49(3):2325-2333. doi:
    2. Zhang Y, Wang D, Peng M, et al. Single‐cell RNA sequencing in cancer research. Journal of Experimental & Clinical Cancer Research. 2021;40(1). doi:
    3. Reggiardo RE, Sreelakshmi Velandi M, Peddu V, et al. Profiling of repetitive RNA sequences in the blood plasma of patients with cancer. Nature Biomedical Engineering. Published online 2023. doi:
    4. Lei Y, Tang R, Xu J, et al. Applications of single-cell sequencing in cancer research: progress and perspectives. Journal of Hematology & Oncology. 2021;14(1). doi:
    5. Xie N, Shen G, Gao W, et al. Neoantigens: promising targets for cancer therapy. Signal Transduction and Targeted Therapy. 2023;8(1). doi:
    6. Zhang Y, Remillard D, Onubogu U, et al. Collateral lethality between HDAC1 and HDAC2 exploits cancer-specific NuRD complex vulnerabilities. Nature Structural & Molecular Biology. 2023;30(8):1160-1171. doi:
    7. Muhar M, Ebert A, Neumann T, et al. SLAM-seq defines direct gene-regulatory functions of the BRD4-MYC axis. Science. 2018;360(6390):800-805. doi:
    8. Mayor-Ruiz C, Bauer S, Brand M, et al. Rational discovery of molecular glue degraders via scalable chemical profiling. Nature Chemical Biology. 2020;16(11):1199-1207. doi:
    9. Levy R, Alter Regev T, Paes W, et al. Large-Scale Immunopeptidome Analysis Reveals Recurrent Posttranslational Splicing of Cancer and Immune-Associated Genes. Molecular & Cellular Proteomics. 2023;22(4). doi:
    10. Rouanne M, Adam J, Radulescu C, et al. BCG therapy downregulates HLA-I on malignant cells to subvert antitumor immune responses in bladder cancer. Journal of Clinical Investigation. 2022;132(12). doi:
    11. Jin N, Kan CM, Pei XM, et al. Cell-free circulating tumor RNAs in plasma as the potential prognostic biomarkers in colorectal cancer. Frontiers in Oncology. 2023;13. doi:
    12. Ju S, Chen C, Zhang J, et al. Detection of circulating tumor cells: opportunities and challenges. Biomarker Research. 2022;10(1). doi:
    13. Zhang H, Lin X, Huang Y, et al. Detection Methods and Clinical Applications of Circulating Tumor Cells in Breast Cancer. Frontiers in Oncology. 2021;11. doi:
    14. Dohmen J, Baranovskii A, Ronen J, et al. Identifying tumor cells at the single-cell level using machine learning. Genome Biology. 2022;23(1). doi:
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    About the Author


    seqadmin Benjamin Atha holds a B.A. in biology from Hood College and an M.S. in biological sciences from Towson University. With over 9 years of hands-on laboratory experience, he's well-versed in next-generation sequencing systems. Ben is currently the editor for SEQanswers. Find out more about seqadmin

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