Transcriptomics and Solid Tumors: The Next Frontier in Precision Cancer Medicine.

Transcriptomics, which encompasses assessments of alternative splicing and alternative polyadenylation, identification of fusion transcripts, explorations of noncoding RNAs, transcript annotation, and discovery of novel transcripts, is a valuable tool for understanding cancer mechanisms and identifying biomarkers. Recent advances in high-throughput technologies have enabled large-scale gene expression profiling. Importantly, RNA expression profiling of tumor tissue has been successfully used to determine clinically actionable molecular alterations. The WINTHER precision medicine clinical trial was the first prospective trial in diverse solid malignancies that assessed both genomics and transcriptomics to match treatments to specific molecular alterations. The use of transcriptome analysis in WINTHER and other trials increased the number of targetable -omic changes compared to genomic profiling alone. Other applications of transcriptomics involve the evaluation of tumor and circulating noncoding RNAs as predictive and prognostic biomarkers, the improvement of risk stratification by the use of prognostic and predictive multigene assays, the identification of fusion transcripts that drive tumors, and an improved understanding of the impact of DNA changes as some genomic alterations are silenced at the RNA level. Finally, RNA sequencing and gene expression analysis have been incorporated into clinical trials to identify markers predicting response to immunotherapy. Many issues regarding the complexity of the analysis, its reproducibility and variability, and the interpretation of the results still need to be addressed. The integration of transcriptomics with genomics, proteomics, epigenetics, and tumor immune profiling will improve biomarker discovery and our understanding of disease mechanisms and, thereby, accelerate the implementation of precision oncology.


Background
Recent advances in technology have improved our understanding of carcinogenesis and led to the discovery of novel therapeutic targets.
Precision oncology combines data from tumor genomic profiling, cell-free DNA assays, proteomic and immune profile analyses, and assessments of other markers to individualize treatment according to unique patient and tumor characteristics. 1 alterations that are successfully targeted by novel treatments. 3,4, [6][7][8][9] Comprehensive gene panels are currently being used to identify molecular therapeutic targets and prognostic and predictive biomarkers and prospective clinical trials are assessing the value of molecular testing in treatment selection across various tumor types. [10][11][12][13][14][15][16][17][18][19] Despite this significant progress in the implementation of precision oncology, several challenges need to be addressed in clinical research and practice. First, it is critical to enhance our knowledge of tumor biology, the mechanisms of carcinogenesis, and driver alterations to improve our ability 4 to identify robust prognostic and predictive biomarkers. Additionally, to date, only a few molecular alterations have been successfully targeted by novel agents. 20-23 Many of these alterations are rare, and a large number of patients need to be screened to identify a single potential therapeutic target. 24 Indeed, the proportion of patients who are matched to therapy in precision oncology trials generally ranges from 5% to 50% and often depends on whether the study is conducted in a specialized clinic with access to novel agents, off-label drug use, timely molecular profiling, and the expertise of clinical trial leaders in genomics. 3-7,24-31 Therefore, our understanding of cancer complexity dictates that additional precision oncology methodologies need to be incorporated to enhance patienttreatment matching and prevent the development of treatment resistance.
Transcriptomic analyses have been included in precision oncology trials only recently and infrequently (Table 1). 7,32-34 Transcriptomics refers to the study of all the RNA transcripts in a cell population, typically by using high-throughput technologies, namely microarrays and RNA sequencing (RNA-seq). 35 In contrast to analysis of DNA sequencing data, the assessment of RNA status and measurement of transcripts can correlate gene expression with biologic activity and cellular status ( Table 2). [32][33][34] Gene expression, in turn, is influenced by genetic and epigenetic factors, such as DNA methylation and histone modifications. Early results of clinical trials suggest that transcriptomic analysis can increase the number of patients matched to drugs. 7 Therefore, transcriptomics is a potentially valuable, though 5 underused, technique for unraveling the underlying mechanisms of cancer and moving towards the implementation of precision oncology. 6

History
Early methods to assess gene expression included Northern blotting, reverse transcriptase quantitative polymerase chain reaction (RT-qPCR), and sequencing of short nucleotide arrays (expressed sequence tags) that were generated from complementary DNAs (cDNAs). However, these methods were developed to evaluate limited numbers of transcripts and are inadequate for comprehensive RNA profiling. Subsequently, serial analysis of gene expression 65  In multicellular organisms, the same genes, and thus the same genome, are found in almost every cell. Not every gene is transcriptionally active in every cell, however, and different patterns of gene expression appear in different types of cells. In addition, multiple RNA variants can be produced by a single gene owing to alternative splicing, RNA editing, or alternative transcription initiation and termination sites. The total transcriptional activity, that is, the full range of RNA molecules expressed, is reflected in the transcriptome of an organism. The transcriptome can be represented as the percentage of the genetic code that is transcribed into RNA molecules, which is estimated to be less than 5% of the genome in humans. 72 In contrast to the genome, the transcriptome changes in response to cellular cues. Indeed, an organism's transcriptome varies dynamically depending on many factors, including environmental conditions and developmental stage.

Epitranscriptomics
Epitranscriptomics, also known as RNA epigenetics, describes the diverse posttranscriptional modifications occurring in cellular RNA. This dynamic processing occurs during RNA maturation under the regulation of RNA-binding proteins. Τo date, more than 150 types of RNA modifications have been identified, including RNA methylation and editing. 73 While the exact role of these modifications is still under investigation, studies have shown that it extends from maintaining the structure of RNA to regulating 8 critical cell systems and that disruptions in RNA processing are associated with various diseases, including cancer. 74,75 Research focusing on specific modifications has revealed associations between deregulation of RNA processing and cancer progression, aggressive tumor behavior, and deregulated cellular processes. 76,77 Given the oncogenic nature of these modifications, their regulators could be targeted for novel therapies.

Identification of therapeutic targets
Transcriptomic data have been incorporated into many different tumor molecular profiles to increase the number of targetable molecular alterations and provide additional therapeutic options to patients with advanced cancer. 7,32-34,78,79 In one study, gene expression profiling of longitudinally collected primary breast tumors and metastatic lesions identified several highly targetable genes. 78 Administration of therapeutic agents against these alterations in patient-derived xenograft models led to a statistically significant antitumor response vs. controls. In another study of 1049 children and young adults with de novo acute megakaryocytic leukemia, RNA-seq revealed druggable targets. 79 Importantly, in our WINTHER precision medicine clinical trial, which prospectively assessed both genomics and transcriptomics in diverse solid malignancies, the use of transcriptomic analysis increased the number of targetable -omic changes by a third over NGS. 7 9

Detection of gene fusions
Conventional cytogenetic analyses, including fluorescence in situ hybridization and RT-qPCR, have been widely used for fusion gene detection.
However, these methods are designed to discern the presence of specific known gene fusions, not identify novel ones. Newer techniques and algorithms have been developed for the performance of wide-scale RNA-seq to detect novel gene fusions. [38][39][40] MicroRNA sequencing Preliminary data suggest that this approach might provide useful insights with which to identify patients who are likely to have a response to therapy. [105][106][107][108] The clinical appeal of the use of noncoding RNAs and miRNAs as biomarkers is that they can be obtained noninvasively via liquid biopsies.
However, the clinical utility of these approaches has yet to be prospectively defined and validated. Independent validation of the signature was successfully performed using 9626 primary tumors. In another study, a cancer type classifier was developed using gene expression data from more than 10 000 tissue samples from 30 tumor types. 56 The accuracy of the classifier was high (77%-88%) and varied according to the primary tumor type, the purity of the tumor sample, and the site of tumor tissue (primary or metastatic). Finally, computational algorithms have been employed to mine RNA expression datasets and identify diagnostic classifiers. 55 Gene expression profiling can be incorporated into diagnostic algorithms for patients with CUP to increase rates of accurate classification and improve understanding of patients' 14 prognoses. However, in randomized trials, treating CUP according to tissueof-origin signatures did not effectively improve outcomes. 121,122

Assessment of tumor heterogeneity
Investigators exploring the intratumor heterogeneity of renal tumors (primary and corresponding metastatic sites) demonstrated that tumors are not only genomically but also transcriptomically heterogeneous. 123 Specifically, they showed that gene expression signatures suggestive of good and poor prognoses can be identified within the same tumor. Others

Prediction of response to immuno-oncology
Despite unprecedented improvement in patient outcomes by the use of immune checkpoint inhibitors, mechanisms of resistance significantly limit the benefit from these treatments. Several genomic alterations are being evaluated as predictive biomarkers for immunotherapy. [128][129][130][131][132] In recent studies, RNA-seq and gene expression analysis have been incorporated to predict responsiveness to immunotherapy. In 1 study, analysis of the genome and transcriptome of melanoma tissue samples identified biomarkers that predicted response to anti-PD-1 therapy. 133 In addition, transcriptomic profiles suggested that innate tumor resistance to anti-PD-1 immunotherapy was associated with mesenchymal and inflammatory tumor phenotypes. 133 In a study of metastatic melanoma tumors, gene expression profiling showed that tumors with PTEN loss had lower expression of inflammation-related genes, suggesting that PTEN loss could be associated with resistance to immunotherapy. 134 Other studies identified gene expression profiles associated with response or resistance to immunotherapeutic agents. 135,136 Transcriptome analysis has also been used to study how the tumor microenvironment evolves after treatment with immunotherapy, 137 as well as tumor immune heterogeneity 136   investigation as a mechanism of therapeutic resistance.

Interrogation of gene expression levels
Gene amplification has frequently been described as a mechanism leading to carcinogenesis. Even though gene amplification would be expected to correlate with overexpression, there are cases where these phenomena are not associated. 64 In order to characterize a gene amplification as a "driver" alteration, overexpression is required. Gene expression profiling alone or in combination with copy-number variation (CNV) analysis is used to identify candidate driver genes for molecular therapeutic targeting. 34

Transcriptomics in clinical trials
The WINTHER trial was one of the first studies to incorporate transcriptional analysis, in addition to genomics, in order to match patients with solid tumors to therapeutic agents ( Table 1) 154 and bioinformatics algorithms aim to eliminate contamination effects. 155 Novel methods have been developed to address the issue of low levels of RNA in archival tissue. Additionally, methodologic artifacts are often encountered in transcriptome analysis and require careful assessment.
Another challenge involves the application of advanced computational methodologies. High-level bioinformatic infrastructure is required to conduct complex analyses of profiling data. For instance, in the WINTHER trial, RNA analysis required the systematic development of an algorithm by bioinformaticians. 7 Therefore, the implementation of transcriptomic analysis in clinical workflows may be more complicated than that of genomic analysis. In addition, reproducibility issues need to be addressed. RNA profiling can be used to compare tumor tissue with normal tissue from the same organ, such as in the WINTHER trial; however, some investigators believe that peripheral blood or buccal swab samples could also be used for comparison. This difference may introduce variability in the interpretation of the results.
Overall, investigators who used transcriptomics in clinical trials developed diverse and complex algorithms for characterizing the actionability of molecular alterations. 7,33,34 Consequently, the use of transcriptomics in clinical practice is arduous and expensive. For instance, in the INFORM study, the average cost per patient for the molecular analysis, including tissue sample shipment, data processing and storage, labor, and general costs, was approximately €7,000. 33 The time from tissue processing to start of analysis ranged from 0 to 112 days. 33 Therefore, transcriptomic 26 analysis requires significant optimization, validation and cost decrease in order to be optimally implemented in clinical practice. Standardization of bioinformatic analysis through expert consensus would make the use of transcriptomic analysis in routine clinical practice more consistent.  Abbreviations: NGS = next-generation sequencing, PFS = progression-free survival, OS = overall survival, pts = patients