Gene Expression Analysis Advances Oncology Research
Oncology researchers are making significant advances in the field by approaching throughput, sampling, and quantification challenges with the creative usage of existing gene expression protocols and emerging tools. Moreover, these advances provide insight for those seeking answers to their research questions.
Next-Generation Sequencing (NGS)
The high throughput of several NGS technologies offers the ability to generate comprehensive genomic and transcriptomic profiles of various cancers. This allows researchers to target libraries of genetic mutations associated with those cancers for quantification and potential therapeutic application.
One group of researchers examining acute myeloid leukemia (AML) used ultra-deep targeted next-generation DNA sequencing of 54 genes commonly reported for somatic mutations.1 In light of practical challenges such as sample availability and turnaround, these scientists employed amplicon-based targeted sequencing to decrease the amount of DNA required and shorten turnaround time. Using samples from 26 myeloid neoplasm patients, their results revealed several novel non-silent somatic mutations previously unseen in AML and provided new potential targets for AML management.
In consideration of the advantages of liquid biopsies, NGS is being applied to analyze cfDNA and ctDNA. For example, researchers studying tumor genotyping in non-small cell lung cancer (NSCLC) piloted a novel NGS approach to detect driver mutations and rearrangements in cfDNA of patients with NSCLC to analyze low-concentration cfDNA, multiplexing across several genes and reducing false positives.2 Termed “bias-corrected NGS”, this pilot study describes a protocol that can detect a wide range of genomic alterations with no false positives, including point mutations, insertions/deletions, and rearrangements.
Quantitative PCR (qPCR)
qPCR is one of the most common methods for quantifying DNA and transcripts. Though there are some more challenging aspects of the technique, particularly in regard to sensitivity and reliance on standard curves, it remains a useful tool for amplifying low target numbers. Indeed, recent research in oncology has shown several innovations utilizing qPCR.
Breast cancer researchers have long been interested in circulating tumor cells (CTCs). However, given their rarity in patients' peripheral blood, isolating CTCs has been challenging. Thanks to advances in qPCR and reverse transcription qPCR (RT-qPCR), CTCs are now detectable and can be characterized. One group of researchers utilized multiplex and singleplex RT-qPCR to examine biomarker genes in CTCs of patients with early-stage breast cancer.3 Their results revealed overexpression of several key transcripts and provided potential prognostic information for patients with early-stage breast cancer.
qPCR has seen considerable utility in validating and quantifying NGS results. For example, one group of researchers examined the pharmacodynamic mechanisms of APR-246, a TP53- targeting compound with anticancer activity.4 Their study examined the effects of APR-246 on global gene expression in human breast cancer cell lines. Using RNA-Seq, researchers found that APR-246 induced the upregulation of six genes in a cell line–dependent manner. These findings were further corroborated by qPCR. Overall, this study provided insight into how combining NGS with qPCR can reveal potential therapeutic targets for anticancer pharmacology.
Droplet Digital PCR (ddPCR)
ddPCR technology is a breakthrough gene profiling technology that allows for increased accuracy and precision, absolute quantification, and higher sensitivity. The capabilities of ddPCR technology are particularly useful in oncology, given that this research often involves limited samples, low abundance targets, rare variants, and subtle genetic alterations.
In qPCR, sample contamination often results in uninterpretable data, particularly when examining low-abundance targets. In an excellent demonstration of how ddPCR can overcome these challenges, one study showed that ddPCR technology could precisely and accurately quantify cDNA in samples with contaminant-dependent data variability, whereas qPCR fell short.5
Analysis of ctDNA from liquid biopsies is a highly attractive avenue for oncology. However, the low abundance of ctDNA relative to cfDNA makes examination using standard qPCR protocols challenging. In a 2018 study, researchers took advantage of the capabilities of ddPCR technology to investigate ctDNA from patients with malignant pleural mesothelioma (MPM).6 The results demonstrated that Droplet Digital PCR could detect ctDNA, even with high background levels of non-target DNA, opening up avenues for ctDNA as a biomarker for MPM diagnosis, treatment, and monitoring.
Oncology researchers have also gained considerable insight by combining NGS with ddPCR technology. For example, one group used NGS and Droplet Digital PCR to analyze changes in cell-free RNA (cfRNA) — a relatively understudied source of biomarkers — in several early-stage cancers.7 Their NGS results found upregulation of specific transcripts in NSCLC and pancreatic cancer samples. From there, they were able to use ddPCR technology to not only detect cfRNA, but also to discriminate between patients and healthy controls based on yield and transcript abundance, particularly in patients with pancreatic cancer. This study provides insight into the use of cfRNA in the early diagnosis of some cancers.
- Shahid, S et al. (2020). Novel Genetic Variations in Acute Myeloid Leukemia in Pakistani Population. Front Genet 11, 560.
- Paweletz, CP et al. (2016). Bias-Corrected Targeted Next-Generation Sequencing for Rapid, Multiplexed Detection of Actionable Alterations in Cell-Free DNA from Advanced Lung Cancer Patients. Clin Cancer Res 22, 915-922.
- Strati, A et al. (2019). Prognostic Significance of TWIST1, CD24, CD44, and ALDH1 Transcript Quantification in EpCAM-Positive Circulating Tumor Cells from Early Stage Breast Cancer Patients. Cells 8(7):652.
- Synnott, NC et al. (2018). The Mutant p53-Targeting Compound APR-246 Induces ROS- Modulating Genes in Breast Cancer Cells. Transl Oncol 11, 1343-1349.
- Taylor, SC et al. (2017). Droplet Digital PCR versus qPCR for gene expression analysis with low abundant targets: from variable nonsense to publication quality data. Sci Rep 7, 2409.
- Hylebos, M et al. (2018). Tumor-specific genetic variants can be detected in circulating cell-free DNA of malignant pleural mesothelioma patients. Lung Cancer 124, 19-22.
- Metzenmacher, M et al. (2020). Plasma Next Generation Sequencing and Droplet Digital-qPCR-Based Quantification of Circulating Cell-Free RNA for Noninvasive Early Detection of Cancer. Cancers (Basel) 12(2):353.