Cancer transcriptome profiling at the juncture of clinical translation

Key Points

  • RNA sequencing (RNA-seq) has the potential to bridge tumour genotypes (for example, mutations) and their phenotypic consequences (for example, cancer molecular subtypes).

  • The field of transcriptomics has matured thanks to lockstep developments in experimental protocols, algorithms and databases.

  • Methodological and algorithmic advances continue to enable clinical applications of transcriptome profiling.

  • Detection of gene fusions is the most immediate application of RNA-seq.

  • Gene expression signatures have demonstrated prognostic and predictive value.

  • Transcriptome profiling will be essential for immuno-oncology.

Abstract

Methodological breakthroughs over the past four decades have repeatedly revolutionized transcriptome profiling. Using RNA sequencing (RNA-seq), it has now become possible to sequence and quantify the transcriptional outputs of individual cells or thousands of samples. These transcriptomes provide a link between cellular phenotypes and their molecular underpinnings, such as mutations. In the context of cancer, this link represents an opportunity to dissect the complexity and heterogeneity of tumours and to discover new biomarkers or therapeutic strategies. Here, we review the rationale, methodology and translational impact of transcriptome profiling in cancer.

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Figure 1: A historical timeline of transcriptomics.
Figure 2: Transcriptome profiling for genetic causes and functional phenotypic readouts.
Figure 3: Tumour phenotypes beyond differential expression.
Figure 4: Paths to clinical translation for RNA-based assays.

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Acknowledgements

The authors thank S. Ellison for assistance in writing, editing and preparing this manuscript. A.M.C. is a Howard Hughes Medical Institute investigator and American Cancer Society professor. M.C. is a Prostate Cancer Foundation Young Investigator.

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Both authors made substantial contributions to the discussion of content and reviewing and editing the manuscript before submission. M.C. was primarily involved in researching data for the article, and A.M.C. was involved in writing the manuscript.

Corresponding author

Correspondence to Arul M. Chinnaiyan.

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PowerPoint slides

Glossary

RNA sequencing

(RNA-seq). An encompassing term for all cDNA profiling techniques using high-throughput sequencing.

cDNAs

DNA molecules obtained through reverse transcription of RNAs.

Expressed sequence tags

(ESTs). Short fragments of a cDNA sequence that identify (tag) a transcript.

Microarrays

A method of cDNA profiling through hybridization and fluorescent labelling.

Serial analysis of gene expression

(SAGE). An economical technique for sequencing very short tags (11 nucleotides) from multiple cDNAs in one Sanger sequencing run.

Digital gene expression

A high-throughput, low-cost technique for expression profiling that involves sequencing short tags rather than the whole transcript.

Unique molecular identifiers

(UMIs). Sequences that are unique to each reverse-transcribed cDNA. PCR duplicates share the same UMI.

NanoString

A barcoding-based and imaging-based technique for the detection and quantification of hundreds of transcripts.

Epitranscriptomics

The study of biochemical modifications of RNA molecules.

Passenger mutations

Mutations that have no measurable effect on the growth of a clone.

Allele-specific expression

(ASE). The analysis of differences in the expression from both alleles, that is, expression variation between the two haplotypes. Also known as allelic imbalance.

Cap analysis of gene expression

(CAGE). A molecular technique to sequence the 5′ end of transcripts.

PAM50

Prediction analysis of microarray 50. A gene expression signature to classify breast cancer into intrinsic subtypes.

Driver mutations

Mutations that provide the cancer with a strong selective advantage, that is, mutations that result in the clonal growth of mutant cells.

Clinical utility

Whether a test has a substantial effect on the diagnosis, prognosis or treatment of a patient.

Allelic dropout

When a sample is sequenced and one or more alleles are not detected.

Analytical validity

The ability to accurately detect and measure the biomarker of interest.

Clinical validity

The clinical performance of a test, that is, how well the test is able to identify the clinical variable of interest (for example, disease status).

Neoantigens

Antigens, herein short peptides, not previously recognized by the immune system. They can be formed by somatic mutations during tumorigenesis.

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Cieślik, M., Chinnaiyan, A. Cancer transcriptome profiling at the juncture of clinical translation. Nat Rev Genet 19, 93–109 (2018). https://doi.org/10.1038/nrg.2017.96

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