Introductory Chapter: Molecular Diagnostic in Cancer

1. Introduction

The Genomic Era in cancer medicine started after the completion of the first human genome study in 2003. Modern pathology and cancer diagnostics now rely on molecular testing to identify genetic alterations that inform diagnosis, prognosis, and treatment decisions. Targeted therapy, immunotherapy, and genomic medicine have become crucial in cancer management. In this introductory chapter, we elucidate the past, present, and potential future of molecular diagnostics to provide a comprehensive overview of molecular diagnostic technologies, testing platforms, and their applications in the treatment of cancer [1].

The term “molecular diagnostics” refers to a group of methods that can be used to identify genetic variations, help in cancer classification and progression prediction, and track therapy efficacy [2].

The earliest molecular diagnostics methods were created in research laboratories in the middle of the previous century. In the early stages of molecular diagnosis, methods, such as recombinant DNA and complementary DNA (cDNA) cloning, were applied to investigate gene sequences [3]. Sanger sequencing was first introduced in 1977 and, for many years, it was the global standard for gene sequencing in clinical laboratories. Polymerase chain reaction (PCR) was later established, and numerous related techniques were subsequently created. In 2000, the first next-generation sequencing (NGS) technology was launched [3], whereas the introduction of the Illumina HiSeq and ThermoFisher Ion Torrent sequencers in 2010 completely paved the way for massively parallel DNA sequencing. Long-read sequencing, also referred to as third-generation sequencing, was the subject of further innovation. The nanopore sequencer is an example of a third-generation sequencer that has the potential to drastically alter the field of molecular diagnostics and nucleic acid sequencing in the near future [4].

2. Modern molecular diagnostic techniques

2.1 Sanger sequencing

Modern molecular diagnostic techniques involve, first of all, a variety of sequencing methods, including Sanger sequencing. Also known as chain termination sequencing, it was once the most widely used sequencing technique and is still considered the gold standard. Frederick Sanger, who received the 1980 Nobel Prize in Chemistry, invented this method for nucleic acid sequencing. The sequencing reaction involves DNA polymerase, primers, deoxynucleic acid, and dideoxy nucleic acid. During sequence extension, the incorporation of dideoxy nucleic acid randomly causes the process to end. After capillary electrophoresis, the collection of fragments of different lengths is analyzed to determine the bases using signals from dideoxy nucleic acid fluorescence [4, 5, 6].

Sanger sequencing is a method used to identify single-gene or single-locus mutations quickly. For instance, EGFR and KRAS mutations in lung cancer, BCR::ABL1 mutations in chronic myeloid leukemia, and KIT and PDGFRA mutations in gastrointestinal stromal tumors can be detected using this method. However, the process is slow and takes a long time. Furthermore, the sensitivity is low; the sensitivity of detection for Sanger sequencing is commonly acknowledged to be around 15–20% mutant allele frequency. This implies that in order to achieve reliable detection, approximately 15–20% of the DNA molecules being sequenced must carry the mutation. To prevent the sequencing of wild-type fragments, locked nucleic acid probes can be used, which can raise the limit of detection to 1% or even lower [7].

2.2 Polymerase chain reaction (PCR)

In 1983, Dr. Mullis created a revolutionary technique called the polymerase chain reaction (PCR), which earned him the 1993 Nobel Prize in Chemistry [8]. The reaction involves several components, including DNA polymerase, primers, nucleotides, and a double-stranded DNA template. After denaturation of the template, primers bind to it and extend from the 5′ to the 3′ end, the newly formed DNA copies serve as templates for further replication, leading to exponential amplification of the original template in a chain reaction. The amplified product can be directly observed using gel electrophoresis and subjected to various analyses, such as melting curve analysis, fragment analysis, and restriction fragment length polymorphism (RFLP) analyses based on the size of PCR fragments [9]; or subjected to melting curve analysis based on its dissociation properties [10].

Sanger sequencing, pyrosequencing, single-base extension, and NGS are further methods for sequencing the PCR results. The majority of these techniques include fluorescent tags in the PCR product that allow optical equipment to spot genomic alterations.

Restriction fragment length polymorphism and fragment analysis are both versions of the same method. Both procedures include amplification of a template by PCR using fluorescently labeled primers and sorting of the PCR products by fragment size (length) using capillary electrophoresis [3].

Small and medium-sized insertions and deletions (50 bases to hundreds of bases long) can be quickly found using fragment analysis, some of which may be difficult to find using other technologies or may be easily missed by huge parallel sequencing.

Restriction fragment length polymorphism is also a cost-effective, fast, and easy technique for detecting whether a given location has single-nucleotide variations or methylation, by using sequence-specific restriction enzymes that separate PCR fragments based on the presence of particular palindromic sequences.

This technique has been used in various cancer molecular diagnostics, including clonality studies of lymphomas, and identification of NPM1 and FLT3 mutations in acute myeloid leukemia [3].

2.3 Quantitative PCR (qPCR)

The development of quantitative real-time PCR (qPCR) was a significant improvement over traditional PCR. Unlike PCR, which measures the result of amplification, qPCR examines the number of DNA copies during the exponential phase of the reaction, giving a more accurate reflection of the initial template amount [11]. In qRt-PCR, reverse transcription (RT) of RNA to complementary DNA (cDNA) and quantitative detection are two commonly combined techniques for gene expression analysis [12]. These techniques employ fluorescent dye-tipped probes that are examined by an optical system, and the number of copies in the starting material is then determined using standard curves or comparison thresholds. qRT-PCR is widely used for minimal disease detection in chronic myeloid leukemia to monitor the BCR::ABL1 fusion, in acute myeloid leukemia for NPM1 mutation, RUNX1::RUNX1T1 and PML::RARA fusions [3].

2.4 Digital PCR (dPCR)

Digital PCR (dPCR) is a relatively new technology that allows for the direct quantification of amplified nucleic acid. Unlike traditional PCR, which amplifies the entire sample in a single reaction, dPCR amplifies thousands or millions of partitions from a single sample [13]. This means that the competitive inhibition, which can be a limiting factor in traditional PCR, has less of an impact on the results, resulting in increased sensitivity of detection [3, 13]. dPCR is commonly used in gene expression analysis, absolute quantification, copy number variation (CNV) detection, and mutation identification. It is also useful for examining liquid biopsy samples due to its high sensitivity.

2.5 Next-generation sequencing

Over a decade ago, next-generation sequencing (NGS), also (probably more properly) named massive parallel sequencing, was developed aiming at enhancing simultaneous detection and capturing all genomic alterations. Recently, it has gained increasing use in clinical practice [14]. The most widespread sequencing platforms are currently the ones employed by Illumina and Ion Torrent [15].

Different chemistries distinguish these two sequencers. Ion Torrent measures changes in electric current as variations in pH, while Illumina detects fluorescence signals. Four fluorescently tagged deoxyribonucleotide triphosphates (dNTPs) of various colors are used to copy the templates on a flow cell during Illumina sequencing. Only the base that is complementary to the template is integrated into the expanding chain or sequencing primer during one reaction round. A laser excites the fluorescent base, and the integrated camera records this distinctive emission spectrum. The template’s sequence is identified by analyzing readout of the signals that appear at the same point in sequential photographs.

The Ion Torrent platform uses current, instead of light, as a signal to read DNA fragments. To clonally amplify the DNA library fragments, a bead’s surface is utilized. During the sequence reading of each fragment in the bead, the deoxyribonucleotide triphosphate is incorporated into the template DNA by a semiconductor chip with micromachined wells. This chip has an ion-sensitive layer and an ion sensor that detects the hydrogen ions generated during the process.

Enrichment techniques like hybrid capture and amplicon capture are employed by clinical laboratories to select regions of interest for targeted sequencing. These methods have proven to be more cost-effective and time-saving than single-gene tests while also improving assay sensitivity and providing better support for therapeutic decision-making and patient management [16]. Targeted NGS tests have become crucial for identifying cancer driver mutations, assessing microsatellite instability, and determining tumor mutation load.

2.5.1 RNA sequencing

Next-generation sequencing is a powerful tool that can identify gene fusions and evaluate gene expression through the RNA sequencing (RNA-seq) method. Gene fusions are mutations that drive many cancer types, especially those involving the tyrosine kinase domain of growth factors [17]. These mutations can be predictive (being targets), diagnostic, or both [18, 19]. However, targeted DNA sequencing (DNA-seq) has limitations for clinical applications as it cannot detect all structural variants since they often involve long introns that are difficult to map or have repetitive elements. RNA-seq, on the other hand, offers a simple and effective method to identify fusions by capturing the junction of exons from the two fusion partner genes. In order to further improve the detection sensitivity of targeted RNA-seq, sequence-specific primers are often used for known partner genes and universal primers for unknown genes [19].

2.5.2 Nanopore sequencing

Technology for nanopore sequencing was created in 2014. It is now involved in academic research. Nanopore sequencing, in contrast to other sequencing technologies that were already accessible in clinical laboratories, does not call for PCR amplification, may yield long reads (10–100 kb), lower costs, and amplification mistakes, and enhance the quality of de novo assembly and mapping. Portable, quick, and inexpensive nanopore sequencing equipment is available [20]. Future developments in virology, gene fusion testing, and germline mutation identification are anticipated as a result of the clinical application of this approach. Additionally, it is anticipated that this technique will be applied at the point of care in the field because these devices are portable. However, the precision and accuracy of nanopore sequencing are still comparatively poor, and efforts are being made to improve them before commercial use [21].

2.5.3 Single-cell sequencing

The use of single-cell sequencing technology is also expanding. Single-cell DNA sequencing analyzes DNA sequences and genomic mutations at the level of individual cells, collects data on temporal and spatial heterogeneity within a particular tumor, and offers details on the development, recurrence, and metastasis of the tumor [22]. By providing details on gene and protein expression, single-cell sequencing of RNA or epigenetic modifications helps to explain phenotypic changes in greater detail [23]. Targeted molecular techniques in cancer therapy are increasingly built on genomic, transcriptomic, and epigenetic data gathered at the level of the individual cell.

Single-cell sequencing has the drawback of requiring fresh or frozen tumor samples to separate individual tumor cells. Through the use of flow cytometry cell sorting, its application to hematological malignancies, particularly myeloid malignancies, is more extensive and easier [24, 25].

2.6 Liquid biopsy/cell-free DNA (cfDNA) assay

A cell-free DNA (cfDNA) assay, sometimes referred to as a liquid biopsy, identifies circulating tumor DNA (ctDNA) in the blood. This method has been recently developed for utilization in cancer monitoring, drug response assessment, diagnosis, and even early detection. Liquid biopsy offers a thorough examination of genetic changes in the main tumor and any distant metastases [26]. Samples can be taken more frequently and more easier during the course of the disease than with tissue biopsy, giving an evolving overview of changes in genetic cancer alternation.

Liquid biopsy is a diagnostic test that can involve examining either a single gene, or a small or large panel of genes. The purpose of this test is to detect single-nucleotide changes, tiny insertions and deletions, structural variants, and microsatellite instability. The ratio of total cfDNA to ctDNA in the blood of cancer patients can vary significantly based on the type, stage, and size of the tumor. Different types of tumors shed cells at varying rates. Typically, the concentration of ctDNA in the blood increases with the later stage and size of the tumor. Apart from blood, cfDNA can also be obtained from urine, cerebrospinal fluid (CSF), and other bodily fluids.

The main characteristics of the molecular diagnostic technologies described above are summarized in Table 1.

Molecular techniques	Variant types				Sensitivity (%)
	SNVs	Small indels	CNV	SVs
Sanger sequencing	✓	✓			25
Fragment analysis and RFLP	±	✓			5
Allele-specific PCR	✓				1–5
qPCR	✓	±		±	<1
qRT-PCR			✓	✓	0.001
dPCR	✓			±	<1
NGS-Amplicon capture	✓	✓	±	✓	5–10
NGS-Hybridization capture	✓	✓	✓	✓	2–5
NGS-liquid biopsy	✓	✓	±	✓	<1
NGS-RNA sequencing	✓	±		✓	5

Table 1.

Schematic comparison of the most common molecular techniques.

± = means may or may not be able to detect those variant types; or with limited ability of detection, CNV = copy number variation, NGS = next-generation sequencing, RFLP = restriction fragment length polymorphism, SNV = single-nucleotide variant, SV = structural variant (Adapted from Jinjuan Yao, Qihui (Jim) Zhai. A narrative review of cancer molecular diagnostics: past, present, and future [J] J Bio-X Res, 2022,05(4): 145–150 [3]).

3. Clustered regularly interspaced short palindromic repeat (CRISPR) technology

Clustered regularly interspaced short palindromic repeat (CRISPR) is considered as a new approach for identifying cancer mutations by using gene editing technology. CRISPR-associated protein 12 (CAS12) and CRISPR-associated protein 13 (CAS13) caspases, along with the detector and Sherlock technologies ltd, are used to identify genetic changes at the DNA and RNA levels [27, 28, 29]. When combined with amplification and NGS, CRISPR technology can be used to selectively enhance the mutant allele by shearing wild-type alleles. This increases the detection sensitivity for low-frequency mutations [3].

CRISPR’s readings, stability, mobility, and low cost will make it a useful adjunct to clinical molecular diagnostics.

4. Machine learning (ML) and artificial intelligence (AL)

Large-panel gene expression profile data, sequencing data, including hotspot mutations, insertions, and deletions, focal or genome-wide copy number alterations, structural variants, mutational signatures, and clinical parameters, have been used to develop machine learning (ML) and artificial intelligence (AI) approaches to infer tumor origin.

Researchers from Memorial Sloan Kettering Cancer Center (MSK) have developed an algorithmic classifier using comprehensive genomic profiling data from 7791 tumors that represent 22 different types of cancer. They found that the classifier accurately predicted the type of tumor in 74.1% of an independent cohort of 11,644 patients and in 73.8% of the training set of 7791 patients. This study shows the potential of using machine learning algorithms to improve cancer diagnosis and treatment. In plasma cell-free DNA, the rate of accurate prediction was 75.0%. These results suggest that AI technology can provide additional information on tumor origin, which can further increase the utility of molecular testing, particularly in tumors that have been labeled as cancers of unknown primary type based on histologic and immunophenotypic characteristics [30, 31].

5. Biomarkers’ analysis in cancer diagnostics

The detection of cancer biomarkers and the emergence of associated therapies have led to changes in the fundamentals of molecular testing for solid tumors.

The first targeted medication to be licensed by the Food and Drug Administration (FDA) was trastuzumab, which was used to treat human epidermal growth factor receptor 2 (HER2)-positive metastatic breast cancer in 1998 [32]. It was reported in 2004 that gefitinib and erlotinib were used in the treatment of lung adenocarcinomas with activating mutations in EGFR [33, 34, 35]. Since then, new genetic mutations that are unique to tumors have been discovered and given the green light, including the KRAS-resistant mutation and cetuximab in colorectal cancer [36] and the BRAF V600E mutant and vemurafenib in melanoma and hairy cell leukemia [37]. Additionally, there are other biomarkers such as mutations in ALK, ROS1, KRAS, BRAF, RET, and many more in lung adenocarcinoma [38].

Over 20 years ago, single-gene, single-platform testing was the foundation of molecular diagnostics [39]. Traditional low-throughput testing methods, however, are unable to collect all pertinent biomarkers from the small number of tissue samples needed for clinical care and the expansion of cancer biomarkers and targeted medicines. To record all clinically indicated mutations, molecular diagnostics laboratories have had to create more comprehensive platforms. Large, focused NGS panels, such as Foundation One and MSK-IMPACT (Integrated Mutation Profiling of Actionable Cancer Targets), were introduced in 2011 and 2014, respectively. These two panels use the pan-cancer method, screening all solid tumors regardless of tumor type or whether the tumor has known biomarkers for a broad panel of cancer genes. Many people with various cancer kinds have been discovered using this type of technique who may profit from clinical trials settings.

Molecular laboratories face the challenge of developing a range of testing platforms to meet clinical demands for both speed and thoroughness. Neither single-gene testing nor cancer panel testing alone can fulfill these requirements. As a result, an algorithm is sometimes necessary for testing stratification, particularly in tumor types with multiple targets. For example, in lung adenocarcinoma, single-gene testing is performed to detect epidermal growth factor receptor (EGFR), anaplastic lymphoma kinase (ALK), or Kirsten rat sarcoma viral oncogene homolog (KRAS) alterations. This is followed by NGS testing to identify mutations, copy number, and structural variants in other driver genes, as well as to assess microsatellite instability and tumor mutation burden to complete the biomarker identification process.

6. Conclusions: the future of molecular diagnostics

The field of molecular diagnostics has experienced significant and rapid expansion in recent years and is expected to continue to do so. With the help of precise, sensitive, and rapid detection, initial diagnosis and monitoring of diseases will become easier. Molecular diagnostics advancements in early detection have the potential to shape the future of cancer management.

Acknowledgments

The author is grateful to Dr. Shaymaa Khattab for editing assistance, and to Elvira Baumgartner and the IntechOpen Editorial Team for the precious assistance.

Conflict of interest

“The authors declare no conflict of interest.”

Notes

This book is dedicated to the memory of Prof. Alessandro Piccaluga (1927–1995). Prof. Piccaluga was emeritus Professor of Pathology at Bologna University, where he directed the Institute of Pathologic Anatomy and founded the School of Pathologic Anatomy. He dedicated his career to diagnosis, teaching, and scientific research, giving a substantial contribution to the advancement of lung and kidney pathology.