Changing Technologies of RNA Sequencing and Their Applications in Clinical Oncology

RNA sequencing (RNAseq) is one of the most commonly used techniques in life sciences, and has been widely used in cancer research, drug development, and cancer diagnosis and prognosis. Driven by various biological and technical questions, the techniques of RNAseq have progressed rapidly from bulk RNAseq, laser-captured micro-dissected RNAseq, and single-cell RNAseq to digital spatial RNA profiling, spatial transcriptomics, and direct in situ sequencing. These different technologies have their unique strengths, weaknesses, and suitable applications in the field of clinical oncology. To guide cancer researchers to select the most appropriate RNAseq technique for their biological questions, we will discuss each of these technologies, technical features, and clinical applications in cancer. We will help cancer researchers to understand the key differences of these RNAseq technologies and their optimal applications.

[1]  Patrik L. Ståhl,et al.  Identification and transfer of spatial transcriptomics signatures for cancer diagnosis , 2020, Breast Cancer Research.

[2]  M. Sorokin,et al.  RNA sequencing for research and diagnostics in clinical oncology. , 2020, Seminars in cancer biology.

[3]  Jian-Guo Zhou,et al.  Development and Validation of an RNA-Seq-Based Prognostic Signature in Neuroblastoma , 2019, Front. Oncol..

[4]  M. Lucia,et al.  Distinct tumor microenvironments of lytic and blastic bone metastases in prostate cancer patients , 2019, Journal of Immunotherapy for Cancer.

[5]  Jason Gertz,et al.  FFPEcap-seq: a method for sequencing capped RNAs in formalin-fixed paraffin-embedded samples , 2019, Genome Research.

[6]  J. Hadfield,et al.  RNA sequencing: the teenage years , 2019, Nature Reviews Genetics.

[7]  C. Mazzanti,et al.  Laser Capture Microdissection and RNA-Seq Analysis: High Sensitivity Approaches to Explain Histopathological Heterogeneity in Human Glioblastoma FFPE Archived Tissues , 2019, Front. Oncol..

[8]  R. West,et al.  Gene expression profiling of single cells from archival tissue with laser-capture microdissection and Smart-3SEQ , 2017, Genome Research.

[9]  Development of a miRNA-seq based prognostic signature in lung adenocarcinoma , 2019, BMC Cancer.

[10]  Patrik L. Ståhl,et al.  Barcoded solid-phase RNA capture for Spatial Transcriptomics profiling in mammalian tissue sections , 2018, Nature Protocols.

[11]  K. Janes,et al.  In situ 10-cell RNA sequencing in tissue and tumor biopsy samples , 2018, Scientific Reports.

[12]  A. Broeks,et al.  Neoadjuvant versus adjuvant ipilimumab plus nivolumab in macroscopic stage III melanoma , 2018, Nature Medicine.

[13]  Merrick I Ross,et al.  Neoadjuvant Immune Checkpoint Blockade in High-Risk Resectable Melanoma , 2018, Nature Medicine.

[14]  J. Maaskola,et al.  Spatially Resolved Transcriptomics Enables Dissection of Genetic Heterogeneity in Stage III Cutaneous Malignant Melanoma. , 2018, Cancer research.

[15]  Patrik L. Ståhl,et al.  Spatial maps of prostate cancer transcriptomes reveal an unexplored landscape of heterogeneity , 2018, Nature Communications.

[16]  A. Califano,et al.  Transcriptional deconvolution reveals consistent functional subtypes of pancreatic cancer epithelium and stroma , 2018, bioRxiv.

[17]  Li-Hong Zhou,et al.  Development and validation of an individualized diagnostic signature in thyroid cancer , 2018, Cancer medicine.

[18]  E. Worrell The teenage years , 2018 .

[19]  Lars E. Borm,et al.  The promise of spatial transcriptomics for neuroscience in the era of molecular cell typing , 2017, Science.

[20]  S. Teichmann,et al.  A practical guide to single-cell RNA-sequencing for biomedical research and clinical applications , 2017, Genome Medicine.

[21]  Robert J. Lonigro,et al.  Integrative Clinical Genomics of Metastatic Cancer , 2017, Nature.

[22]  Boxi Kang,et al.  Landscape of Infiltrating T Cells in Liver Cancer Revealed by Single-Cell Sequencing , 2017, Cell.

[23]  S. Dudek,et al.  Optimized Method for Robust Transcriptome Profiling of Minute Tissues Using Laser Capture Microdissection and Low-Input RNA-Seq , 2017, Front. Mol. Neurosci..

[24]  Jeong Eon Lee,et al.  Single-cell RNA-seq enables comprehensive tumour and immune cell profiling in primary breast cancer , 2017, Nature Communications.

[25]  Gang Chen,et al.  Identification of a RNA-Seq based prognostic signature with five lncRNAs for lung squamous cell carcinoma , 2017, Oncotarget.

[26]  N. Hacohen,et al.  Single-cell RNA-seq reveals new types of human blood dendritic cells, monocytes, and progenitors , 2017, Science.

[27]  Ji-Hong Kim,et al.  Association of Uba6-Specific-E2 (USE1) With Lung Tumorigenesis , 2017, Journal of the National Cancer Institute.

[28]  Andrey Alexeyenko,et al.  Spatially resolved transcriptome profiling in model plant species , 2017, Nature Plants.

[29]  Hui Jiang,et al.  Development of a RNA-Seq Based Prognostic Signature in Lung Adenocarcinoma , 2017, Journal of the National Cancer Institute.

[30]  J. Seidman,et al.  Single-Cell Resolution of Temporal Gene Expression during Heart Development. , 2016, Developmental cell.

[31]  Marco Mignardi,et al.  Fourth Generation of Next‐Generation Sequencing Technologies: Promise and Consequences , 2016, Human mutation.

[32]  L. Chin,et al.  Analysis of Immune Signatures in Longitudinal Tumor Samples Yields Insight into Biomarkers of Response and Mechanisms of Resistance to Immune Checkpoint Blockade. , 2016, Cancer discovery.

[33]  Patrik L. Ståhl,et al.  Visualization and analysis of gene expression in tissue sections by spatial transcriptomics , 2016, Science.

[34]  David G. Kirsch,et al.  Application of single-cell RNA sequencing in optimizing a combinatorial therapeutic strategy in metastatic renal cell carcinoma , 2016, Genome Biology.

[35]  Philippe Terrier,et al.  RNA sequencing validation of the Complexity INdex in SARComas prognostic signature. , 2016, European journal of cancer.

[36]  Arul M Chinnaiyan,et al.  Translating cancer genomes and transcriptomes for precision oncology , 2016, CA: a cancer journal for clinicians.

[37]  S. Gabriel,et al.  Genomic correlates of response to CTLA-4 blockade in metastatic melanoma , 2015, Science.

[38]  Nallasivam Palanisamy,et al.  Integrative Clinical Sequencing in the Management of Refractory or Relapsed Cancer in Youth. , 2015, JAMA.

[39]  May D. Wang,et al.  Comparison of RNA-seq and microarray-based models for clinical endpoint prediction , 2015, Genome Biology.

[40]  P. Sharma,et al.  Immune Checkpoint Targeting in Cancer Therapy: Toward Combination Strategies with Curative Potential , 2015, Cell.

[41]  Kun Zhang,et al.  Fluorescent in situ sequencing (FISSEQ) of RNA for gene expression profiling in intact cells and tissues , 2015, Nature Protocols.

[42]  M. Caligo,et al.  Characterization of three alternative transcripts of the BRCA1 gene in patients with breast cancer and a family history of breast and/or ovarian cancer who tested negative for pathogenic mutations , 2015, International journal of molecular medicine.

[43]  C. Sotiriou,et al.  Transfer of clinically relevant gene expression signatures in breast cancer: from Affymetrix microarray to Illumina RNA-Sequencing technology , 2014, BMC Genomics.

[44]  Charles J. Vaske,et al.  Single-cell analyses of transcriptional heterogeneity during drug tolerance transition in cancer cells by RNA sequencing , 2014, Proceedings of the National Academy of Sciences.

[45]  Heather L. Mulder,et al.  Targetable kinase-activating lesions in Ph-like acute lymphoblastic leukemia. , 2014, The New England journal of medicine.

[46]  Nicolas Stransky,et al.  The landscape of kinase fusions in cancer , 2014, Nature Communications.

[47]  Christopher R. Cabanski,et al.  cDNA hybrid capture improves transcriptome analysis on low-input and archived samples. , 2014, The Journal of molecular diagnostics : JMD.

[48]  Ahmad M Khalil,et al.  Identification of mRNAs and lincRNAs associated with lung cancer progression using next-generation RNA sequencing from laser micro-dissected archival FFPE tissue specimens. , 2014, Lung cancer.

[49]  George M. Church,et al.  Highly Multiplexed Subcellular RNA Sequencing in Situ , 2014, Science.

[50]  Jeffrey A. Engelman,et al.  Tyrosine kinase gene rearrangements in epithelial malignancies , 2013, Nature Reviews Cancer.

[51]  Kristin Branson,et al.  JAABA: interactive machine learning for automatic annotation of animal behavior , 2013, Nature Methods.

[52]  Ryan D. Morin,et al.  Genetic alterations activating kinase and cytokine receptor signaling in high-risk acute lymphoblastic leukemia. , 2012, Cancer cell.

[53]  Fatih Ozsolak,et al.  RNA sequencing: advances, challenges and opportunities , 2011, Nature Reviews Genetics.

[54]  M. Gerstein,et al.  RNA-Seq: a revolutionary tool for transcriptomics , 2009, Nature Reviews Genetics.

[55]  S. Liyanarachchi,et al.  Germline Allele-Specific Expression of TGFBR1 Confers an Increased Risk of Colorectal Cancer , 2008, Science.

[56]  Shahin Rafii,et al.  Migratory neighbors and distant invaders: tumor-associated niche cells. , 2008, Genes & development.

[57]  W Brad Barbazuk,et al.  Gene discovery and annotation using LCM-454 transcriptome sequencing. , 2006, Genome research.