Clinically relevant fusion oncogenes: detection and practical implications

Abstract

Mechanistically, chimeric genes result from DNA rearrangements and include parts of preexisting normal genes combined at the genomic junction site. Some rearranged genes encode pathological proteins with altered molecular functions. Those which can aberrantly promote carcinogenesis are called fusion oncogenes. Their formation is not a rare event in human cancers, and many of them were documented in numerous study reports and in specific databases. They may have various molecular peculiarities like increased stability of an oncogenic part, self-activation of tyrosine kinase receptor moiety, and altered transcriptional regulation activities. Currently, tens of low molecular mass inhibitors are approved in cancers as the drugs targeting receptor tyrosine kinase (RTK) oncogenic fusion proteins, that is, including ALK, ABL, EGFR, FGFR1-3, NTRK1-3, MET, RET, ROS1 moieties. Therein, the presence of the respective RTK fusion in the cancer genome is the diagnostic biomarker for drug prescription. However, identification of such fusion oncogenes is challenging as the breakpoint may arise in multiple sites within the gene, and the exact fusion partner is generally unknown. There is no gold standard method for RTK fusion detection, and many alternative experimental techniques are employed nowadays to solve this issue. Among them, RNA-seq-based methods offer an advantage of unbiased high-throughput analysis of only transcribed RTK fusion genes, and of simultaneous finding both fusion partners in a single RNA-seq read. Here we focus on current knowledge of biology and clinical aspects of RTK fusion genes, related databases, and laboratory detection methods.

Genomic instability and cancer evolution

Genomic instability is an exaggerated accumulation of genomic abnormalities and one of the classical cancer hallmarks.^1,2 Genomic instability is associated with both the appearance of ‘driver’ and ‘passenger’ mutations and accelerated molecular evolution of the tumor. There are two main levels of genomic instability.³ At the nucleotide level, it generates mostly single nucleotide substitutions, short insertions and deletions, insertion of transposable elements,⁴ and variability of the microsatellite loci.^5,6 In turn, chromosomal instability implies shuffling of the bigger fragments of the genome and is associated with aneuploidy,⁷ deletions or amplifications of genes,⁸ chromosomal rearrangements,⁷ translocations, and gene fusions.⁹ Chromosomal instability is currently considered one of the most common features of cancer cells. Approximately 90% of solid tumors and more than a half of hematopoietic cancers have large-scale chromosomal aberrations.¹⁰

Genomic instability generates genomic aberrations and can lead to accelerated evolution of cancers toward drug and immune resistance.^11,12 The increased mutation rate can help cancer cells to control drug response by eliminating or amplifying genes related to drug efficacy mechanisms. These processes, underlaying resistance, include loss or gain of chromosomes and their fragments, as well as nucleotide-level mutations. For instance, the reversion mutations of BRCA1/2 in solid cancers make tumors more resistant to platinum or PARPi therapy.¹³ Oncogenic mutations can provide immune evasion, such as mutations in KRAS that promote immune suppression in colorectal cancer.¹⁴ Moreover, high level of cancer cells aneuploidy negatively correlates with the effectiveness of immunotherapy.¹² On the other hand, deficient DNA repair systems can also lead to the increased mutation load in protein coding regions, thus expanding the amount of neoantigens in tumor.¹⁵ This makes tumors susceptible to recognition by immune system, thus potentially enhancing immunotherapy efficiency.¹⁶ Thus, high tumor mutation burden is one of the major biomarkers for immunotherapy prescription.¹⁷

Genomic instability is preceded by accumulation of mutations in proto-oncogenes, tumor suppressor genes, or genetic predisposition that lead to defective regulation of tightly connected mechanisms of DNA damage response and repair,^18–23 cell cycle progression, senescence, and apoptosis,²⁴ that coincide with inefficient elimination of transformed cells by the immune system.^25–27 For example, negative regulators of Wnt pathway that plays a crucial role in cell proliferation, migration, polarity, and cell fate determination, are usually mutated and, consequently, downregulated in lung cancer cells.²⁸ This results in abnormal cell proliferation and can provoke metastasis.²⁸ Immunosuppression can be caused by various random events such as viral infection,^29,30 insufficient generation and CD8 T-cell exhaustion,³¹ and tissue repair associated with M2 macrophage polarization.³²

Chromosomal instability is associated with an ability to move and interconnect different pieces of chromosomes.^33,34 During normal germ cell development, DNA is exchanged or crossed-over between chromosomes during prophase 1 of the meiosis followed by haploid cell division to generate cells with a single copy of each chromosome. These mechanisms are absent in the somatic cells and re-emerge in cancer leading to translocations and aneuploidy.^35–38

One of mechanisms that promotes genomic instability is the so-called replication stress, that is, disturbances in the DNA replication process that lead to the arrest or destruction of the replication fork.³⁹ In turn, termination of replication in the absence of timely repair can provoke DNA double-strand breaks.⁴⁰

Hotspots of the DNA double-strand breaks often occur at specific ‘fragile’ sites on chromosomes.⁴¹ At these loci, there is an increased frequency of loss of homozygosity or heterozygosity in cancer cells.⁴² In regions of nucleotide tandem repeats, mutations also occur due to malfunctions of the replication fork.⁴³ The system repairs DNA double-stranded breaks by the homologous recombination of adjacent genome regions. As a result, duplications of microhomologous chromosomal regions can occur, which are found in many types of cancer, such as ovarian and breast cancers.⁴⁴

Mitotic disorders are another reason for the appearance of genomic instability at the chromosomal level. Errors can occur both in the early and late stages of cell division. The contribution to the development of genomic instability of sister chromatids pairing violations is well known.⁴⁵ Also, mutations in the genes responsible for the passage of cell cycle checkpoints can be associated with genome instability.⁴⁶ A common occurrence in the anaphase of cancer cells is chromosome lagging.⁴⁷ This is usually due to abnormal attachment of chromosomes, when one kinetochore attaches to microtubules extending from different poles of the spindle. Because this phenomenon is not recognized by the spindle assembly checkpoint, this merotelic attachment and the resulting lagging of the chromosome is an important cause of genomic instability.⁴⁶ Mitotic disorders can lead to aneuploidy and even genome-wide duplications.⁴⁸ It is important to note that the ploidy of the genome is directly related to the ability of cells to adapt. Tetraploid cells have more mutations per genome, but not per chromosome, which increases the adaptability of the cell.⁴⁹ In addition, it has been experimentally shown that genome-wide duplications increase the resistance of cells to subsequent manifestations of genomic instability.⁴⁹

Telomere changes can also lead to genomic instability.⁵⁰ Thus, shortening of telomeric regions is a frequent event in the early stages of tumor development.⁵¹ Normally, it occurs as a result of the accumulation of the effect of previous acts of progenitor cell replication, and when the threshold value is crossed, such cells undergo aging and/or are eliminated. However, mutations in the cancer cell, for instance, leading to loss of function of negative regulators of cell proliferation, can help to avoid apoptosis.⁵² Alternatively, mutations can result in function gain for the different protein families that inhibit apoptosis, for example, members of IAP and Bcl-2 protein families, or other prosurvival factors.⁵³ This has a strong potential to evade programmed cell death.⁵⁴ Telomere shortening can also be associated with various manifestations of genomic instability at the chromosomal level, from amplification or deletion of individual loci to changes in entire chromosomes.⁵⁵ Late-stage tumors typically reactivate telomerase or activate an alternative telomere elongation pathway.⁵⁰ The latter mechanism appears to be associated with significant genomic rearrangements and defects in DNA repair systems.⁵⁶

In addition, high expression and insertions of LINE-1 and other active transposable elements within accessible chromatin regions generate multiple somatic variants, and contribute to the genomic instability in cancers.^57–60 Moreover, epigenetic factors can also cause and promote genomic instability. The key epigenetic factors contributing to gene expression regulation and mutagenesis are DNA methylation, histone modification, nucleosome remodeling, and non-coding RNAs.⁶¹

Novel fusion genes in cancer

Chromosomal translocations, deletions, insertions, and inversions can lead to the formation of chimeric genes with oncogenic functions. The first chimeric gene was described within the so-called Philadelphia chromosome, which is formed as a result of the translocation of regions of chromosomes 9 and 22 [t (9;22) (q 34; q 11.2)] and occurs in approximately 90% of all cases of chronic myeloid leukemia.^62,63 As a result of structural rearrangement, a chimeric gene BCR-ABL1 is formed, which encodes the corresponding chimeric protein BCR-ABL.⁶⁴ It is a continuously active tyrosine kinase that permanently activates proliferation, replication, differentiation, and also renders the cell resistant to apoptosis.⁶⁴ Active proliferation can lead to an increased rate of mutagenesis, which contributes to the development of drug resistance in cancer cells.⁶⁵

Since the discovery of the Philadelphia chromosome, many other chimeric genes have been found that are specific to a particular type of cancer.⁶⁵ For example, the chimeric EWSR1-FLI gene is characteristic for a number of sarcomas.^66,67 KIAA1549-BRAF fusion oncogene is characteristic for spinal intramedullary astrocytomas,⁶⁸ and fusions with JAZF1 and YWHAE genes frequently occur in leiomyosarcomas.^69,70 In many cases, fusion genes are just passenger mutations accompanying carcinogenesis that do not play any significant role in cancer progression.^71–73 On the other hand, some fusion genes harbor specific tumor promoting molecular activities and are, therefore, referred to as driver mutations.⁷⁴ Their tumor-promoting activities may strongly differ in nature and are still most probably not completely understood.

However, even for the fusion oncogenes, only a tiny fraction of them currently serves as the targets for cancer therapeutics. All such chimeras are fusions with receptor tyrosine kinase (RTK) genes. Presence of such fusion gene in the genome can be a significant biomarker both for the diagnosis and prognosis of the disease, and for the choice of therapy. Tens of small molecular mass therapeutics that inhibit kinase activities of these gene products have been approved by the US FDA for treatment of tumors with confirmed chimeric genes (Table 1). In particular, chimeric transcripts of genes for RTKs ALK, FGFR 1-4, NTRK 1-3, RET, ROS1, and MET are used for prescription of the respective targeted therapeutics (Table 1).

Table 1.

US FDA-approved cancer drugs targeting fusion genes. Only three out of 35 drugs approved for BCR-ABL1 fusion are shown (Data collected from https://nctr-crs.fda.gov/fdalabel/ui/search).

Cancers	Targeted kinase fusion partners	Drug	Approval, year	Reference
Ph+ CMLa	BCR-ABL1	Imatinib	2001	Amarante-Mendes et al.75, Milojkovic and Apperle76
Ph+ CMLa	BCR-ABL1	Nilotinib	2010	Amarante-Mendes et al.75, Radich et al.77
NSCLC ALK or ROS1 positiveb, ALCL ALK positivec	ALK, ROS1, MET	Crizotinib	2011	Shaw et al.,78 Solomon et al.79
NSCLC ALK positived	ALK, ROS1	Ceritinib	2014	Facchinetti et al.80
NSCLC ALK positived	ALK, ROS1, EGFR	Brigatinib	2017	Descourt et al.81
NSCLC ALK positive	ALK	Alectinib	2017	Wang et al.82
Solid tumors with NTRK gene fusions	NTRK1-3	Larotrectinib	2018	Rudzinski et al.83
NSCLC ROS1 positivee, solid tumors with NTRK fusions	NTRK, ROS1, ALK	Entrectinib	2019	Dziadziuszko et al.,84 Doebele et al.85
Urothelial cancer with FGFR3 fusion	FGFR3	Erdafitinib	2019	Zengin et al.86
Cholangiocarcinoma with FGFR2 fusion	FGFR1-3	Pemigatinib	2020	Abou-Alfa et al.87
RET-positive NSCLC, thyroid cancerf	RET	Pralsetinib	2020	Gainor et al.88
RET-positive NSCLC, thyroid cancerf	RET, VEGFR1, VEGFR3	Selpercatinib	2020	Subbiah et al.89
NSCLC ALK positive	ALK	Lorlatinib	2021	Sehgal et al.,90 Descourt et al.81
Cholangiocarcinoma with FGFR2 fusion	FGFR2	Infigratinib	2021	Javle et al.91
Ph+ CMLa	BCR-ABL1	Asciminib	2021	Yeung et al.92

^aPh⁺ CML – chronic myeloid leukemias with Philadelphia chromosome.

^bNSCLC ALK or ROS1 positive – non-small-cell lung cancers harboring translocations of ALK or ROS1 genes.

^cALCL ALK positive – anaplastic large-cell lymphoma, systemic ALK-Positive.

^dNSCLC ALK positive – non-small-cell lung cancers with translocation of ALK gene.

^eNSCLC ROS1 positive – non-small-cell lung cancers with translocation of ROS1 gene.

^fRET-positive NSCLC – non-small-cell lung cancers with translocation of RET gene.

For these fusion oncogenes, all paternal tyrosine kinase receptor proteins have a set of common structural features (Figure 1). They consist of (at least) an extracellular ligand-binding domain, transmembrane domain, and cytoplasmic tyrosine kinase domains.^93,94

Figure 1.

Life cycle of the tyrosine kinase receptor. Ligand binding to RTK monomer mediates receptor dimerization, autophosphorylation, and various adapter protein binding. In turn, RTK interacting proteins mediate receptor internalization and downstream RAS/MAPK cascade activation including negative and positive feedback loops regulating ubiquitination and phosphorylation. Internalized RTK continues signaling and upon ubiquitination can be recycled to the cell surface.^95–98 Alternatively, RTK can be subjected to lysosomal degradation.^96,98

MAPK, mitogen-activated protein kinase; RTK, receptor tyrosine kinase.

Binding of the ligand to the extracellular part of the receptor causes dimerization and transphosphorylation of tyrosine residues of the intracellular domain. This leads to activation of the kinase domain and subsequent triggering of various intracellular cascades.⁹⁹ In this case, the signaling pathway of mitogen-activated protein kinase (MAPK pathway), the phosphoinositide-3-kinase/protein kinase B (PI3K/AKT/mTOR), protein kinase C (PKC), and STAT-dependent pathways are activated, which are responsible for the induction of proliferation and cell survival.^100–103 Breakage and ligation during chimera formation occur in the intron region and, thus, due to splicing, the exon–intron structure of both parts of the transcript is preserved. Ligand binding leads to autophosphorylation and adaptor protein binding that further phosphorylate and ubiquitinate receptor which is subsequently internalized into lysosomes followed by either recycling or degradation,^96,97 Internalized RTK continues to signal inside the cell.

There are two main types of structure of chimeric tyrosine kinase genes (Figure 2). In the first case, the N-terminal extracellular and transmembrane domains-encoding part of a tyrosine kinase receptor gene may be replaced by a partner gene, which results in the presence of tyrosine kinase domain is the 3′-moiety of a chimeric gene. In this case, the 5′-partner domains, as a rule, contribute to the dimerization of the tyrosine kinase domain. For example, the coiled coil motif, sterile alpha domain, LIS 1-homologous, IMD domain, and caspase domain can be mentioned as the contributing structural motifs of the 5′ fusion partner.¹⁰⁴ This can lead to ligand-independent dimerization of the tyrosine kinase domain which triggers further carcinogenic properties of such fusion oncogene.

Figure 2.

Structure and functions of the tyrosine kinase gene fusions of the first and second types.¹⁰⁹ (a) Type 1 fusion diagram represents a fusion protein between EML4 and ALK retaining the tyrosine kinase domain, whereas the rest of the RTK including transmembrane domain is lost. The resulting chimera translocates into the cytoplasm where it signals in a RAS/MAPK-dependent manner forming lipid-independent protein granules.^110,111 ELM4 is a spindle checkpoint protein¹¹² whose trimerization domain is retained in chimeras and most likely mediates interaction with the spindle assembly checkpoint complex and mitotic defects.^113,114 (b) Type 2 RTK diagram exemplifies FGFR3-TACC3 chimera in which TACC3 dimerization leucine zipper is attached to the C-terminus of FGFR3 mediating ligand-independent dimerization and signaling. In turn, TACC3 is a spindle checkpoint protein and FGFR3-TACC3 chimera causes mitotic defects.^115,116

MAPK, mitogen-activated protein kinase; RTK, receptor tyrosine kinase.

In the second type, the tyrosine kinase receptor is the 5′-terminal partner, and the 3′-partner most probably can additionally stabilize the chimeric RNA or protein product.¹⁰⁵ The breakpoint in such case is usually located after the exons encoding the tyrosine kinase domain. Thus, the partner gene is fused to the C-terminus of a nearly full-length tyrosine kinase receptor.¹⁰⁵

Importantly, for both types, the main feature is the preservation of the active tyrosine kinase domain. This appears to be a key factor distinguishing a ‘driver’ mutation from random chimeric products arisen as a side effect of genomic instability.¹⁰⁶ The second factor is the preservation of an open reading frame for both parts of the chimera. Both factors are important to distinguish between the ‘driver’ and ‘passenger’ gene fusion events. For example, in infant hemispheric glioma, only the patients with preserved open reading frame for a ZCCHC8-ROS1 fusion were responding to entrectinib.¹⁰⁷ Note also the published outstanding case of heavily pretreated glioblastoma which expressed transcripts for four clinically relevant fusion transcripts: with ALK, FGFR2, NTRK2, and NTRK3 genes. Due to tumor heterogeneity, these were, however, expressed each by only a minor fraction of tumor cells, and the prescription of the corresponding targeted therapies would be most likely unsuccessful.¹⁰⁸

Fibroblast growth factor receptor gene fusions

Fibroblast growth factor receptors 1–4 (FGFR1–4) are a highly conserved family of transmembrane tyrosine kinases that can form chimeric oncogenes in various tumors. Normally, FGFR family members play important roles in cell proliferation, embryonal development, organogenesis, maintenance of homeostasis, and tissue integrity.¹¹⁷ In turn, structural aberrations of FGFR family genes contribute to oncogenesis, tumor progression, and development of drug resistance. In about 8% of the cases, abnormal increase in FGFR activities in cancers is thought to be caused by their gene fusions.¹¹⁸

Many chimeric gene partners of the FGFR family have been described.¹⁰⁵ The FGFR1-HOOK3 and FGFR1-TACC1 chimeras have been described for cases of the gastrointestinal stromal tumor and gliomas.¹¹⁹ FGFR1-ZNF703 chimeric gene is found in breast cancer.¹²⁰ The products of these chimeras include the N-terminal portion of the FGFR1 protein and the coiled coil motif at the C-terminal, which induces dimerization of the tyrosine kinase triggering various signaling cascades.

Among all family members, FGFR2 gene is more likely than others to form chimeras.¹¹⁸ As a result of the fusion, the normally prohibited activation of FGFR2 and the launch of various signaling cascades also occur, which can stimulate oncogenesis. FGFR2 fusions are characteristic for cholangiocarcinomas, where they occur in 10–16% of the cases.¹²¹ Thus, in patients with cholangiocarcinoma, the chimeric genes FGFR2–AHCYL, FGFR2–BICC1, FGFR2–PPHLN1, and FGFR2–TACC have been identified and functionally characterized.¹⁰⁴ The chimeric FGFR2-CCDC6 gene can initiate the proliferation of cancer cells in vivo.¹²² Furthermore, more than a hundred of FGFR2 chimeric partners have been identified but not studied in detail. Such partners include KIAA1217, KIAA1598, DDX21, LAMC1, NRAP, NOL4, PHC1, RABGAP1L, RASAL2, ROCK1, TFEC, AFF4, CELF2, DCTN2, DNAJC12, DZIP1, FOXP1, INA, KCTD1, LGSN, and other genes.¹²³ In addition to cholangiocarcinoma, chimeric FGFR2 genes are also frequently found in colorectal cancer, lung cancer, and in hepatocarcinomas.^124,125

In turn, FGFR3 fusions are characteristic for glioblastoma, lung, and bladder cancers.¹²⁶ One of the best studied chimeras is FGFR3-TACC3.¹¹⁵ The product of this chimeric gene is a fusion of the N-terminal region of the FGFR3 with the coiled coil domain of TACC3.¹¹⁵ Coiled coil motif is located at the C-terminus of the protein and is normally involved in the formation and stabilization of the mitotic spindle.¹²⁷ The FGFR3-TACC3 fusion has been found in different types of cancer: in gliomas, in cancers of the lung, bladder, head and neck, and cervix.^125,128,129 The formation of this chimeric oncogene results in constitutive activation of the FGFR3 tyrosine kinase domain and, as a consequence, activation of the MEK/ERK and STAT1 signaling pathways.¹²⁹ Also, the chimeric protein FGFR3-TACC3 is localized to the mitotic spindle and induces errors in the chromosome segregation process, thus resulting in the appearance of aneuploid cells in glioblastoma.¹¹⁵ In contrast, in bladder cancer cells, FGFR3-TACC3 chimera inhibits TACC3 localization to mitotic spindle, thereby contributing to aneuploidy.¹¹⁶ FGFR3 fusions with other genes have also been detected but have not been characterized in-depth. For example, the product of the FGFR3–BAIAP2L1 chimera was found in bladder and lung cancers, and the AES, ELAVL3, JAKMIP1, TNIP2, and WHSC1 genes are also known among the confirmed FGFR3 fusion partners.^118,130

Fusions with FGFR genes are important prognostic molecular markers. A study was made of tumors of the bile ducts: 152 cholangiocarcinomas and 4 intraductal papillary mucinous tumors were analyzed by fluorescent in situ hybridization (FISH) for the presence of FGFR2 chimeras.¹³¹ Totally, 13 tumors carrying FGFR2 translocations were found which showed statistically significantly longer overall survival: 123 versus 37 months for the patients without translocations.¹³¹ Similar results were obtained in a study of 377 patients with biliary tract cancer, of which 63 had FGFR2 chimeras.¹³²

Neurotrophic TRK family gene fusions

The neurotrophic TRK (NTRK) family includes TRKA, TRKB, and TRKC proteins which are encoded by the NTRK1, NTRK2, and NTRK3 genes, respectively.¹³³ Normally, these tyrosine kinases play a pivotal role in neuronal survival and CNS plasticity.¹³⁴

Various mutations of the NTRK family genes in cancer cells have been described leading to single nucleotide substitutions, amplifications, and abnormal splice isoforms.¹³⁵ However, the formation of chimeric oncogenes is the most common mutation type that leads to increased kinase activity. NTRK gene sequences including a tyrosine kinase domain are usually located at the 3′ end of the chimeras, and are fused to the 5′ region of a partner gene.¹³⁶ The product of the chimeric gene is an oncoprotein capable of activating the tyrosine kinase domain without the involvement of a ligand binding.¹³⁷ Various NTRK chimeric 5′ partners are known, including ETV6, TPM3, and LMNA, yet their exact roles in the function of an oncoprotein have not been fully characterized.¹³⁸

NTRK fusion oncogenes were found in multiple types of cancer. At the same time, their occurrence is relatively low. The analysis of 13,467 adult and pediatric tumor samples from The Cancer Genome Atlas (TCGA) and St. Jude PeCan databases showed that the frequency of chimeric NTRK genes in the most common cancer types was less than 1%.¹³⁹ Another study by Rosen and colleagues showed that, when present, functional chimeric NTRK genes are expressed from the very beginning until the most advanced stages of carcinogenesis, thereby indicating a ‘driver’ rather than a ‘passenger’ nature of the mutation.¹⁴⁰

RET gene fusions

RET mutations such as amplifications, single nucleotide substitutions, insertions and deletions, and the formation of chimeric genes have been described in cancer cells. Chimeras with the 3′ RET moiety are more frequently detected and better described. In this case, the tyrosine kinase domain part is usually placed under the control of a stronger promoter, which leads to its overexpression.¹⁰⁶ Many 5′ fusion partners of RET have been described, of which the most common are CCDC6, NCOA4, and KIF5B.^103,141,142 RET activation as a result of chimera formation can occur through different mechanisms. First, as already noted, the substitution of the 5′-part of RET for a gene fragment with a stronger promoter. Second, dimerization of RET tyrosine kinase domains due to fusion with partners providing a coiled-coil motif, as in the case of CCDC6 gene. As a result, ligand-independent kinase activation occurs.¹⁴³ Also, oncogenic hyperactivation of RET in a fusion is possible due to the loss of the autoinhibitory N-terminal region.¹³⁰

ROS1, ALK, and MET gene fusions

ROS1 fusions

ROS1 is a proto-oncogene encoding a tyrosine kinase with unknown physiological function. The first ROS1 chimeric gene was found in the U118MG glioblastoma cell line in 1987.¹⁴⁴ Interestingly, the tyrosine kinase domain of ROS1 has high structural identity with another RTK, ALK.⁷⁸ Impaired expression of ROS1 is known for many cancer types, and at least 55 5′-terminal partners of the ROS1 chimeras were reported.¹⁴⁵ The frequency of occurrence of 5′-partners in the chimeras may depend on the cancer type. In particular, large heterogeneity of the 5′-partners is characteristic of glioblastoma, non-small-cell lung cancer (NSCLC), and of inflammatory myofibroblastic tumors. In NSCLC tumors, the most common 5′-partners for the ROS1 chimeras are CD74 (~44%), EZR (16%), SDC4 (14%), and SLC34A2 (10%).¹⁴⁵

ROS1 chimeras are formed due to both intra- and interchromosomal rearrangements. For example, in glioblastoma, the chimeras are more often the result of intrachromosomal translocations, and in NSCLC, they are instead interchromosomal.^146,147

The most common structure of the ROS1 chimera includes the loss of virtually the entire extracellular domain of ROS1 and the fusion with the 5′ part of the partner gene, retaining the open reading frame and a complete intracellular kinase domain.¹⁴⁵ As before, the formation of chimeras usually results in ligand-independent, constitutive activation of the ROS1 tyrosine kinase domain. The intracellular localization of the chimeric ROS1 gene depends on the 5′-partner gene and influences the activation of specific signaling pathways. For example, the SDC4-ROS1 and SLC34A2-ROS1 chimeras are localized in endosomes and activate the MAPK pathway more efficiently than the CD74-ROS chimera localized in the endoplasmic reticulum.¹⁴⁸ It was shown in mice models that the presence of the chimeric ROS1 gene alone may be sufficient to induce carcinogenesis. However, when combined with the loss of tumor suppressor p16^Ink4a, this results in development of a more aggressive tumor.¹⁴⁹

ALK fusions

Also, many tumors including large cell lymphoma, diffuse large B-cell lymphoma, glioma, NSCLC, colorectal cancer, breast, ovarian, and esophageal cancer have chimeras with tyrosine kinase ALK.¹⁵⁰ More than 90 5′-partner genes of ALK have been described,^150–152. ALK translocations are not uncommon and are found in approximately 8% of all NSCLCs.¹⁵³ As a result of structural rearrangements, the chimeras retain a full-fledged ALK tyrosine kinase domain at the C-terminus. As in other cases, dimerization of chimeras results in aberrant persistent kinase domain activity.¹⁵⁴ ELM4-ALK is the most frequent ALK fusion in NSCLC.¹⁵⁵ ELM4 is a spindle checkpoint protein required for the proper chromosome alignment and attachment of microtubules to the kinetochore.¹¹² Accordingly, ELM4-ALK fusion inhibits spindle assembly checkpoint control, that is, inhibits cell cycle arrest in response to the paclitaxel and leads to the mitotic errors (Figure 2), ^113,114 This effect is partially due to the kinase activity of ALK domain and, most likely, other oncogenic ALK fusions have acquired kinase activity as well.¹¹⁴

MET fusions

The MET gene is another important RTK-encoding proto-oncogene. The formation of chimeras with MET is a relatively rare event, but such products are found in various cancer types. The first described chimeric gene was TPR-MET.¹⁵⁶ The replacement of MET extracellular and juxtamembrane domains containing regulatory regions by TPR two leucine zipper domains leads to a constitutively dimerized and therefore activated Met kinase domain.¹⁵⁶ The dimerization domains are essential for TPR-MET oncogene transforming activity, as well as the absence of MET extracellular and juxtamembrane domains.¹⁵⁶ Similar to many other RTKs, the increased MET activity promotes activation of downstream intracellular pathways and signaling axes RAS-MAPK and PI3K-AKT.¹⁵⁷

Structural rearrangements involving MET were detected in 0.5% of cases of NSCLC, 3% of glioblastomas, and isolated cases were described in secretory carcinoma of the salivary gland and pediatric fibrosarcoma.^158–161 Several partners of MET chimeras have been described: the HLA-DRB1, KIF5B, PTPRZ1, STARD3NL, and ST7 genes.¹⁶² The stability and degradation of the MET receptor is regulated by an intracellular peri-membrane domain encoded by exon 14.¹⁶³ Interestingly, the formation of TPR-MEt also resulted in the loss of exon 14 of MET without disturbing the reading frame. Mutations resulting in the loss of exon 14 in the MET mRNA occur in 3–5% of NSCLCs.¹⁶⁴ This can be caused by both structural rearrangements and point mutations leading to splicing disorders.¹⁶⁵ Mutations are detected both in the intron region around exon 14 and directly in the splicing sites. After pre-mRNA maturation, a mutant MET receptor with an increased lifetime is then translated.¹⁶³ These mutations are characteristic of lung adenocarcinomas; however, they are also found in other types of cancer: pulmonary sarcomatoid (pleomorphic) carcinomas, squamous cell NSCLC, and less often in gliomas and other tumors.^165,166

Application of protein fusion inhibitors in clinical practice

The recent findings revealed overall better response rate to the approved TKIs in patients with different target RTK fusion genes.¹⁶⁷ Thus, development of new and broader testing of already approved drugs in more than 100 clinical trials targeting 26 gene fusions is currently underway (Table 2).

Table 2.

Drugs tested against neoplasms with gene fusions that are currently in clinical trials (clinicaltrials.org). Please refer to the website for complete information regarding treatment modalities and specific groups of patients in a specific clinical trial.

Cancer type	Targeted drug(s)	Fusion gene partner(s)*	Clinical trial ID
AML	Bosutinib monohydrate, decitabine, enasidenib mesylate, gilteritinib fumarate, glasdegib maleate, ivosidenib, venetoclax	BCR-ABL1	NCT04655391
Bladder cancer	Neratinib	HER2-GRB7	NCT01956253
Bladder cancer with mutation in FGFR3 gene	Erdafitinib	FGFR3	NCT04917809
Solid cancers with fusions of NTRK, ROS1, ALK genes	Entrectinib	NTRK genes, ROS1, ALK	NCT03066661
Cholangiocarcinoma	E79	FGFR genes	NCT04238715
Cholangiocarcinoma with mutations of FGF or FGFR genes	Futibatinib	FGFR genes	NCT02052778
Cholangiocarcinoma with mutations in FGFR2 gene	Infigratinib (BGJ398)	FGFR2	NCT02150967
Cholangiocarcinoma with mutations in FGFR2 gene	Infigratinib (BGJ398), gemcitabine, cisplatin	FGFR2	NCT03773302
CML	Imatinib withdrawal, dasatinib, nilotinib	BCR-ABL1	NCT04147533
CML	Dasatinib, nilotinib	BCR-ABL1	NCT03079505
CML	LBH589	BCR-ABL1	NCT00451035
CML	PF-114	BCR-ABL1	NCT02885766
CML, ALL	Nilotinib	BCR-ABL1	NCT01077544
Clear cell sarcoma	AMG 337	EWSR1-ATF1	NCT03132155
Epithelioid hemangioendothelioma	Trametinib	TAZ-CAMTA1	NCT03148275
Fibrosarcoma with NTRK fusion	Larotrectinib, standard of care	NTRK genes	NCT05236257
Glioblastoma, IDH-wildtype	Metformin and radiation therapy, temozolomide	FGFR3-TACC3	NCT04945148
Glioma	PLB11	PTPRZ1-MET	NCT02978261
Intrahepatic cholangiocarcinoma	Derazantinib	FGFR genes	NCT03230318
Malignant hepatobiliary neoplasm	Ponatinib hydrochloride	FGFR2	NCT02265341
Myeloproliferative disorders	Imatinib	PDGFR	NCT00038675
Non squamous lung cancer	Crizotinib	ALK	NCT01154140
Non-squamous, NSCLC	Afatinib	NRG1	NCT04750824
NRG1-rearranged malignancies	Afatinib	NRG1	NCT04410653
NSCLC	TPX-131	ALK	NCT04849273
NSCLC	Tarloxotinib bromide	ERBB	NCT03805841
NSCLC	Pemigatinib	FGFR genes	NCT05210946
NSCLC	Toripalimab injection combined with axitinib	negative: EGFR mutation, ALK, and ROS1 fusions	NCT04459663
NSCLC	Tarloxotinib bromide	NRG1, ERBB	NCT03805841
NSCLC	XZP-5955 tablets	NTRK genes	NCT04996121
NSCLC	Apatinib single agent arm	RET	NCT02540824
NSCLC	Vandetanib	RET	NCT01823068
NSCLC	Cabozantinib	RET, ROS1, NTRK genes	NCT01639508
NSCLC	AB-16	ROS1	NCT04395677
NSCLC with ALK and ROS1 aberrations	Carboplatin + Pemetrexed + Atezolizumab + Bevacizumab, Carboplatin + Pemetrexed + Atezolizumab	ALK, ROS1	NCT04042558
NSCLC	PF-234166	ALK	NCT00932451
NSCLC	Selpercatinib, placebo	RET	NCT04819100
NSCLC with ALK rearrangement	Crizotinib	ALK	NCT02201992
NSCLC and thyroid neoplasms	LOXO-26	RET	NCT05241834
NSCLC and thyroid neoplasms	LOXO-26	RET	NCT05225259
Ovarian cancer and carcinosarcoma	Pamiparib	ABCB1	NCT03933761
Pancreatic cancer	Seribantumab	NRG1	NCT04790695
Prostate cancer	Cytarabine	ETS	NCT00480090
Prostate cancer	ESK981	ETS	NCT03456804
Prostate cancer	Androgen deprivation therapy	TMPRSS2-ETS	NCT02303327
Prostate cancer	Enzalutamide	TMPRSS2-ETS	NCT02288936
Prostate cancer	Enzalutamide, abiraterone, carboplatin, cabazitaxel, docetaxel, radium chloride Ra-223, niraparib plus abiraterone acetate plus prednisone	RET	NCT03903835
Recurrent IDH-wildtype glioma with FGFR(1-3)-TACC3 fusion	AZD4547	FGFR(1–3)-TACC3	NCT02824133
Renal cell carcinoma with Xp11.2 translocation and TFE3 fusion	Cabozantinib, sunitinib	TFE3	NCT03541902
Renal cell carcinoma with Xp11.2 translocation and TFE3 fusion	Axitinib, nivolumab	TFE3	NCT03595124
Solid tumors	Debio 1347	EGFR	NCT03834220
Solid tumors	Derazantinib	EGFR	NCT01752920
Solid tumors	Erdafitinib	FGFR	NCT04083976
Solid tumors	HMBD-1	NRG1	NCT05057013
Solid tumors	Seribantumab	NRG1	NCT04383210
Solid tumors	AB-16	NTRK genes	NCT04617054
Solid tumors	Entrectinib	NTRK genes	NCT02568267
Solid tumors	ONO-7579	NTRK genes	NCT03182257
Solid tumors	DS-651b	NTRK genes, ROS1	NCT02675491
Solid tumors	BOS172738	RET	NCT03780517
Solid tumors	KL59586	RET	NCT05265091
Cholangiocarcinoma	Infigratinib	EGFR	NCT04233567
NSCLC with RET rearrangements	Pralsetinib	RET	NCT03037385
Solid tumors	Pembrolizumab	EGFR, ALK, ROS1	NCT03049618
Solid tumors	Afatinib	NRG1	NCT05107193
Solid tumors with NTRK gene fusions	Larotrectinib	NTRK genes	NCT02576431
Solid tumors with NTRK, ROS1, ALK gene fusions	SIM183-1A	NTRK genes	NCT04671849
NF2-deficient solid tumors with YAP1, TAZ gene fusions	IK-93	YAP1, TAZ genes	NCT05228015
CNS tumors	Entrectinib	FGFR genes	NCT02650401
Solid tumors with FGFR alterations	E79	FGFR genes	NCT04962867
Solid tumors with NTRK gene fusions	Larotrectinib	NTRK genes	NCT03025360, NCT04945330, NCT02637687, NCT02122913, NCT04142437, NCT05192642
Solid tumors with NTRK gene fusions	Selitrectinib	NTRK genes	NCT03206931, NCT04275960, NCT03215511
Thyroid cancers with PAX8-PPARG fusion	Pioglitazone	PAX8-PPARG	NCT01655719
Upper tract urothelial carcinomas, urothelial bladder cancer	Infigratinib	FGFR genes	NCT04197986
Urothelial carcinoma	AZD4547	FGFR2/3	NCT05086666

^*Some rows of the column contain only one fusion gene in case the clinical trial was conducted regardless the second fusion partner gene.

ALL, acute lymphoblastic leukemia; AML, acute myeloid leukemia; CML, chronic myeloid leukemia; FGFR, fibroblast growth factor receptor; NSCLC, non-small-cell lung cancer.

Accordingly, the presence of chimeric genes for RTKs is one of the key biomarkers facilitating the choice of therapy. For example, the drug larotrectinib is approved by the US FDA for tumors harboring chimeric NTRK genes.¹⁶⁸ Enterectinib is another drug with a similar profile of action, with somewhat broader activity⁸⁵: it can also inhibit ROS1 and ALK chimeric products.¹³⁰ In addition, broad-spectrum tyrosine kinase inhibitors were the first drugs to treat tumors with FGFR chimeras: dovitinib, lenvatinib, lucitanib, nintedanib, derazantinib, and ponatinib. In addition to their activity against FGFR, they can also inhibit VEGFR, RET, KIT, and PDGFR.

However, drugs with low specificity may cause more serious side effects.¹⁶⁹ Therefore, more precise inhibitors have been developed that act specifically against mutations of FGFR family members, including fusion formations. Two selective inhibitors for the treatment of tumors with FGFR chimeric genes have been approved by the US FDA. Erdafitinib is approved for the treatment of urothelial carcinomas with FGFR2 and FGFR3 mutations, which included the chimeric gene FGFR3-TACC3.¹⁷⁰

Selective RET inhibitors pralsetinib (BLU-667, NCT03037385) and selpercatinib (LOXO-292, NCT03157128) in early-stage clinical trials of NSCLC with RET fusions resulted in 56% overall response rate for pralsetinib.^88,89 LOXO-292 was recently approved by the US FDA for the treatment of lung and thyroid cancers with RET driver mutations, with respect to fusions.¹⁷¹ In addition, cases have been described of the acquisition of drug resistance to tyrosine kinase inhibitors in tumors with EGFR mutations, which occurs as a result of structural rearrangements of RET.¹⁷² This also highlights the importance of RET analysis for selecting the optimal treatment strategy.

For the treatment of NSCLC with ROS1 rearrangements, crizotinib has been approved in multiple countries, with an overall response rate of 65–80%.⁷⁸ Crizotinib is also the first targeted drug for the treatment of tumors with ALK rearrangements.⁷⁹

Entrectinib is an US FDA-approved drug for the treatment of NSCLC with ROS1 rearrangements.⁸⁴ Entrectinib is able to penetrate the blood–brain barrier, which is especially important in the treatment of tumors that have metastasized to the brain. Currently, clinical trials investigate the activity of entrectinib against tumors with ROS1 fusions regardless of the cancer type. It was possible to observe objective responses, for example, even for the cases of melanoma and high-grade gliomas.^173,174

Crizotinib, ceritinib, alectinib, brigatinib, and lorlatinib are approved by the US FDA for the treatment of ALK chimeric lung cancer. Response to ALK-inhibiting therapy has also been described in studies on renal cell and colon cancers.¹⁷³ Of note, a retrospective clinical study showed that progression-free survival after treatment of ALK-positive NSCLC depends on the 5′-partner of the chimera with the 3′-part of ALK.¹⁷⁵ However, the partner genes of the ALK chimeras are not currently routinely taken into consideration when prescribing therapy.

In addition, the drugs capmatinib and tepotinib have recently been approved for the treatment of NSCLC with MET mutations resulting in exon 14 skipping.¹⁷⁵

Experimental methods for identification of fusion oncogenes

Identification of fusion oncogenes is a non-trivial task because there are many alternative methods available that can produce results which may contradict each other (Table 3). It is important, therefore, to carefully select method(s) of choice for the experimental testing considering availability of biomaterials, costs, equipment, high throughput, time of analysis, and even complexity of bioinformatic data interpretation.

Table 3.

Summary of advantages and disadvantages of common methods for detection of gene fusions.

Method	Advantages	Disadvantages	References
FISH	Diagnostic gold standard, high sensitivity and specificity	Laborious. Visual quantification, difficult to automate, requires pre-defined knowledge of fusions. Low resolution, problematic detection of fusions within the same chromosome. Cannot detect fusions with novel partners	Thompson et al.,176 Nguyen et al.,177 Ali et al.,178 Cruz-Rico et al.,180 De Luca et al.,184
IHC	Common detection method	Detects protein overexpression, but not fusions	Thorne-Nuzzo et al.,185 Boyle et al.,186 Shia.256 Zhang.257
RT-PCR methods	Diagnostic gold standard, excellent sensitivity and specificity, widely accepted and cheap. Easy to multiplex and automate. Straightforward interpretation, best suited for clinical laboratory analysis	Requires information about location of fusion breakpoint. Cannot detect novel fusions	Ali et al.,178 Cruz-Rico et al.,180 Lyu et al.,192 Sorber et al.,194 Abbou et al.,195 Shelton et al.196
DNA sequencing of tumors	Allows unbiased detection of mutations and fusions. Covers both transcribed and non-coding regulatory elements of the genome. For example, allows detection of an active promoter – oncogenic transcript fusions	All NGS methods require bioinformatics support. Analytical methods are not standardized yet. Detects expressed and non-expressed fusions. The whole genome deep sequencing is still expensive. Lower sensitivity compared to RNA-seq-based assays	Gerstung et al.,258 ICGC/TCGA Pan-Cancer Analysis of Whole Genomes Consortium,72 Zhang et al.259
Circulating cell-free DNA sequencing	Minimally invasive analysis of tumor DNA for monitoring of the disease. Straightforward DNA extraction from serum	Relatively high frequency of false-negative results. Accurate sample preparation is needed. Plus, the above disadvantages of DNA sequencing methods	Hofman,212 Dagogo-Jack et al.,162 Blaquier et al.,213 Leighl et al.214
Targeted DNA/RNA sequencing	Relatively high-throughput with high coverage of target fusion sites	Collects only information about tested regions and/or fusion types	Heydt et al.,208 Gasc et al.,219 Zheng et al.220
Bulk RNA-seq	Allows detection of known and novel fusions and the presence of an ORF for possible protein expression. Filters out passenger mutations. Permit analysis of archived FFPE samples. Relatively cheap and achieve high coverage. Allows high throughput. Simultaneously measures level of gene expression	May require further standardization. Can be technically challenging	Sorokin et al.,5 Sorokin et al.,197 Winters et al.,225 Peng et al.240
Single-cell RNA-seq	Measures clonality of tumors. Independently measures gene expression and T-cell receptor heterogeneity of immune cells and quantity and gene expression of other non-cancerous cells	Requires additional equipment for single-cell library preparation. Relatively expensive and requires extensive bioinformatics support. Low coverage in terms of reads per single cell, so very low sensitivity of fusion detection. Clinical benefit is questionable	Rozenblatt-Rosen et al.,241 Zeng et al.,242 Nieto et al.,243 Amir et al.,260 Becht et al.,261 Maynard et al.244

FFPE, formalin-fixed paraffin-embedded; NGS, next-generation sequencing; ORF, open reading frame.

Fluorescent in situ hybridization

FISH is a method based on the hybridization of labeled specific DNA probes. It allows detecting changes in the number of chromosomes, gene fusions, loss of a chromosomal locus or an entire chromosome in both fresh and fixed tumor tissue samples.¹⁷⁶ For example, FISH is used to detect amplifications of the ERBB2 gene for Her2 protein, an important prognostic marker for breast cancer.¹⁷⁷ FISH is also a standard method for detecting the Philadelphia chromosome in chronic myeloid leukemia.¹⁷⁸ In particular, probes for loci that are contiguous in the absence of rearrangements are used to detect translocations. The signal from the native chromosome will be a pair of closely spaced color signals, whereas in the case of rearrangement, the two colors are separated in space.¹⁷⁹

The chimeric EML4-ALK gene is a characteristic biomarker of lung adenocarcinoma. Routine detection of this well-known translocation can also be performed using the FISH method.¹⁸⁰ Likewise, FISH is used to detect known rearrangements of the ETV6 gene in secretory carcinoma and pediatric fibrosarcoma specimens.¹⁸¹ A number of laboratories use this approach to detect NTRK gene rearrangements and other fusions such as the FGFR3-TACC3 chimeric oncogene.^182,183 Nevertheless, FISH diagnostics does not provide information about the functional activity of translocations including preservation of an open reading frame and expression level and does not allow detection of the non-canonical or unknown rearrangements. In addition, this method is poorly sensitive to the detection of local intrachromosomal rearrangements that are characteristic of FGFR2 fusion genes.¹⁸⁴

Immunohistochemistry

Immunohistochemistry ((IHC) method is simple, does not require complex equipment, is relatively affordable, and fast in execution. In addition, reliable results can be obtained using a relatively small amount of starting material. The use of IHC has been approved by the US FDA for the detection of ALK rearrangements in lung cancer samples for selection of patients for treatment with ALK-targeted therapy.¹⁸⁵ Also, a ROS1-specific monoclonal antibody is used to detect ROS1 rearrangements by the IHC.¹⁸⁶

Apparently, IHC method detects not the chimeric gene products themselves, but rather the expression level of the respective tyrosine kinase domain. Thus, increased IHC signal can be registered also due to stronger expression of the whole, non-fusion RTK gene which still can be targeted by the respective specific drug(s). Although this method is easy to perform and inexpensive, its accuracy may be reduced due to technical issues arising from tissue fixation, or other pre-analytical variables.¹⁸⁷ Specifically, comparisons of IHC with FISH and next-generation sequencing (NGS) methods for screening of ALK rearrangements in lung cancer resulted in prevalence of ALK-positive samples tested by IHC.^188–190 Furthermore, most of IHC-positive and FISH-negative samples were shown to be negative by NGS, thus supporting the hypothesis of frequent misleading detection of mutations other than fusions (e.g. amplifications) using IHC.¹⁸⁹ However, some patients who were FISH negative but IHC positive for ALK fusion showed complete or partial responses to ALK-targeted therapy, thus supporting the relevance of IHC application in practice¹⁹⁰ and clinical importance of other factors, such as the overexpression of ALK.¹⁹¹

PCR-based methods

PCR-based methods are widely used both for the diagnosis and for the prognosis and treatment of cancer. Also, reverse transcription PCR is a standard method for confirming the presence of chimeric transcripts resulting from structural rearrangements of chromosomes.^178,180 Several kits are commercially available to detect NTRK, ALK, Ret, and ROS fusions in formalin-fixed paraffin-embedded (FFPE) RNA by the multiples real-time PCR using mutation-specific primers and/or hybridization probes with about 1% or more sensitivity and 100% specificity. Detection of 22 fusion genes in AML patients RNA using a commercial quantitative PCR kit demonstrated 99% concordance with cytogenetic analysis.¹⁹² Nonetheless, an agarose gel electrophoresis was used for measurements of multiple EWSR1-ETS and FUS-ETS fusions in Ewing sarcoma by multiplex RT-PCR (Ueno-Yokohata et al. 2021). In turn, high-resolution capillary electrophoresis was used to detect 9 fusion transcripts in the single multiplex reaction.¹⁹³ Intriguingly, out of 122 patients examined, abnormal size was detected for one sample due to a rear deletion of the ETV6 exon 5 within ETV6-RUNX1 chimeric gene. Interestingly, RT-PCR was used in Idylia GeneFusion assay to detect fusion products of the NTRK gene without knowledge of the fusion partner by measuring an imbalance between 5′ and 3′ end of the gene.¹⁹⁴ The Idylia GeneFusion assay is basically a device that automatically extracts RNA and performs expression analysis directly from the samples including fresh and FFPE samples. The data demonstrated good concordance between Idylia GeneFusion, IHC, and RNA-seq based Oncomine Focus Assay. Another method, droplet digital multiplied PCR, measures the presence of RNA in small droplets, and each of them encompasses a single RNA molecule, allowing ‘YES-NO’ quantification of PCR results. Thereby, RNA is measured by counting positive and negative droplets. This method was recently used to measure gene fusions from as little as 1 pg of RNA with great success.^195,196

To conclude, traditional and novel PCR-based tools represent a gold standard for fusion detection in cancer and are widely used in clinical practice, research, and development.

NGS methods

NGS is an increasingly common method for analyzing tumors. It has a number of advantages over the approaches listed above. Thus, NGS is a more accurate and sensitive method that allows one to analyze various genetic variants in one experimental procedure, eliminating the need to conduct multiple tests in sequence to identify significant biomarkers, thereby significantly reducing the time for selecting therapy.^5,197–199 It should be noted that in order to obtain informative results, NGS technologies require a minimum amount of tumor material; they are also able to capture DNA variants present in a minority of cells.²⁰⁰ To date, many approaches have been proposed for the detection of molecular markers of tumors based on NGS. Although whole genome studies are not widely used in clinical practice, in particular due to their high cost, whole exome analysis, gene panel analysis, and whole transcriptome technologies are increasingly used in the selection of cancer therapies. Methods based on NGS have been developed to search for single nucleotide substitutions, deletions, and duplications of genes, to assess the mutation load of tumors, structural rearrangements of chromosomes, as well as to analyze the level of expression of clinically significant genes and to detect chimeric transcripts.^201,202

Several clinical trials investigating applicability of NGS for prognosis and ultimately for treatment decision can be mentioned including Strata PATH (NCT05097599), GITIC Study (NCT02013089), The MATCH Screening Trial (NCT02465060), MyTACTIC (NCT04632992), and BALLETT (NCT05058937). The methodology for detection of mutations used in these studies is based on the NGS. The aim of these studies was formulated as to determine the presence of molecular targets for the approved drugs (including gene fusions) and to verify whether patients will benefit more from the targeted treatment.

DNA sequencing

Whole genome sequencing (WGS) is the most comprehensive platform for genome profiling.²⁰³ The method has potential to identify the genomic locations of all known and currently unknown fusion events.²⁰⁴ However, application of WGS in tumor biology is nowadays limited to research field due to high costs, lack of standardization, and median turnaround times.

Methods based on whole exome sequencing (WES) are used to detect deletions and duplications of genes, assess the mutational load of tumors, and determine the MSI status.²⁰⁵ Whole exome studies mainly cover only coding regions of the genome (less than 2%), which provides a reduced cost compared to whole genome analysis with sufficient coverage. Despite technical difficulties, the method is widely used to determine various changes in the genome. For example, several approaches have been developed to detect changes in the copy numbers of specific genome regions [copy number aberration (CNA)] using sequencing data.²⁰⁶ Perhaps the optimal approach for the analysis of WES data for CNA is measuring of the sequencing depth of different genome regions.²⁰⁶ If, after normalization, an imbalance in the number of reads in certain part(s) of the genome is detected, then this can be interpreted as a marker of a copy number alteration in the corresponding chromosomal region(s).²⁰⁶ However, this algorithm cannot detect fusion genes. Moreover, WES as a basis for fusion detection tool has a strong technical limitation, namely its focus on gene exons, whereas most of fusion junctions are located in introns. Thus, WES capacity to detect fusion breakpoints is strongly limited. Indeed, the attempt to develop fusion detection tool based on WES data showed lower sensitivity compared to RNA-seq-based approach.²⁰⁷

To detect structural rearrangements of chromosomes that lead to the formation of chimeric genes, a number of targeted DNA sequencing approaches have also been developed. Thus, the panel to detect both known and not yet described chimeric genes for regulatory kinases ALK, ROS1, RET, BRAF, MET, FGFR1-3, and NTRK1-3 has been developed. The panel consists of DNA probes labeled with biotin that are complementary to the intron and exon sequences of the genome, which are known breakpoints in the formation of chimeric genes.²⁰⁸ Similar approaches have been used in commercial panels to search for chimeric genes from different manufacturers. Most fusions occur in intron sequences, which often contain repetitive sequences, such as insertions of transposable element, and are sequenced less efficiently than coding sequences.²⁰⁹ This leads to false-negative results when the presence of the chimeric gene is confirmed by another method, for example, FISH or RNA sequencing.

Davies et al. compared three approaches for detecting rearrangements of ROS1 gene in lung cancer.²¹⁰ Targeted DNA sequencing did not detect 4 out of 18 chimeric genes confirmed by alternative approaches. The authors attribute false-negative results to specific structural features of specific introns.

Also, Benayed et al. analyzed the quality of chimeric genes detection using DNA sequencing panels.²¹¹ 14% of tumors with confirmed chimeric genes for clinically relevant tyrosine kinases were not detected when analyzed by the US FDA approved MSK-IMPACT panel, which is based on biotinylated oligonucleotides to capture genomic sequences of interest. False-negative results are also associated with the structural features of introns.

Sequencing of circulating cell-free DNA

Sequencing of circulating cell-free DNA from plasma of cancer patients revealed the presence of mutations characteristic for tumor DNA.²¹² Sequencing of cell-free DNA from plasma of NSCLC patients with ALK mutation revealed acquisition of additional mutations in ALK and co-occurring amplification of MET1,¹⁶² KRAS amplification, and a PI3KCA E545K mutation.^213,214 This method requires prompt isolation of plasma for DNA extraction and permits non-invasive and cheap monitoring of cancer patients.

RNA sequencing

RNA sequencing is a method that has several advantages over DNA-based approaches. This approach has been proven informative for analyzing the expression level of various genes associated with the effectiveness of the response to anticancer drugs,²¹⁵ activation or inhibition of various molecular pathways.^202,216,217 Approaches for the analysis of the mutational load of tumors according to full transcriptome analysis are also described.^197,218 Therefore, the use of RNA sequencing makes it possible to assess the manifestation of various clinically important biomarkers in one experimental procedure.

The detection of chimeric transcripts has a number of advantages over DNA sequencing approaches. For example, when analyzing transcriptomic profile data, only transcriptionally active chimeric genes are identified, thereby filtering in only those fusions that might be the drivers of cancer progression and leaving out the passenger mutations. Second, in such a way both parts of a fusion gene can be identified at once – equally effective for both known and previously unknown fusions. Third, integrity of an open reading frame can be easily assessed, as well as the presence of a kinase domain in the chimeric gene product.

Targeted RNA sequencing

A variety of targeted panels have been proposed to search for both known and novel chimeric transcripts using RNA sequencing. Two approaches are widely used: selection of genes of interest by hybridization of cDNA with oligonucleotides labeled with biotin and complementary to exons of the target genes; or enrichment of libraries by PCR with specific primers complementary to the exon boundaries of the genes of interest and the universal adapter sequence (Figure 3).^219,220

Figure 3.

Methods for enrichment of sequencing libraries with specific cDNA targets. (a) Enrichment of the library by hybridization to biotinylated oligonucleotide. (b) Enrichment of the library by PCR with a specific primer (modified, according to Kozarewa et al.²²¹ Zheng et al.²²⁰).

In a recent study, Heydt et al. compared four RNA sequencing and one DNA-sequencing-based targeted panels for the detection of chimeric genes from cell lines and FFPE tumor samples.²⁰⁸ As in the studies described above, the DNA-based approach appeared to be less sensitive, producing more false negatives in the analysis of biopsies. Among the alternative RNA analytical panels, three were enriched by amplification: Archer FusionPlex Lung Panel (ArcherDX), QIAseq RNAscan Custom Panel (Qiagen), and Oncomine Focus Assay (Thermo Fisher Scientific). In turn, TruSight Tumor 170 Assay (Illumina) is based on hybridization of target sequences with biotinylated oligonucleotides. The best results were obtained for the TruSight Tumor 170 Assay (Illumina), which detected all chimeric transcripts in the samples under analysis. The single false-negative signal was observed in the results of ArcherDX and Qiagen panels; however, Qiagen panel returned more false-positive chimeras. The Thermo Fisher Scientific assay did not find 7 chimeric genes out of 18. The low sensitivity of the last panel is explained by the fact that the approach is based on classical PCR: both primers are complementary to known sequences of chimeric transcripts, which makes it impossible to detect chimeras that were never described previously and not included in the panel design. Thus, in general, RNA sequencing approaches are technically more effective than DNA analysis for the detection of chimeric oncogenes.²⁰⁸

Methods for generation of sequencing libraries enriched in regions close to susceptible chimerization point are highly sensitive, especially for the detection of known structural rearrangements. However, this approach is limited by the set of probes available in the targeted panels, which does not allow detecting in such a way rearrangements at previously unknown loci. Also, the use of panels limits the set of biomarkers that can be analyzed in one experimental procedure. These shortcomings are devoid of total RNA or mRNA sequencing.

Whole transcriptome RNA sequencing for detection of fusion genes

Genome-wide RNA sequencing evaluates mutations of the transcribed DNA in an unbiased manner. A number of approaches have been proposed for the detection of chimeric transcripts in the analysis of full-transcriptome data. There are two main directions to the analysis of the RNA sequencing data for the detection of chimeric transcripts: (i) alignment of reads per genome and search for those reads that map to different loci or (ii) initial assembly of reads into long transcripts followed by a search for chimeras that do not map entirely to one genome region (Figure 4).²²²

Figure 4.

Scheme of operation of algorithms based on alignment (left) and primary assembly (right) (according to Haas et al.,²²² modified).

In search for chimeric transcripts, Haas and colleagues used both approaches to compare 23 analytic algorithms.²²² The authors showed that the sensitivity of methods based on primary alignment is higher than for the approaches based on assembly of reads. The best analytic tools identified were STAR-Fusion, Arriba, and STAR-SEQR. It is important to note that algorithms for searching of the chimeric reads have been developed primarily for the analysis of cell lines and fresh frozen biopsies.^223,224 Most of the work devoted to assessing their quality was carried out on artificial data, or again data obtained from cell lines and fresh frozen biopsies.^225,226 However, paraffin-embedded biopsies fixed in formalin are the most common type of biomaterial in clinical practice, which is stored in collections for a long time.^227,228

However, RNA in paraffin blocks is more degraded due to the fixation and storage procedures, which can lead to more false-negative results when searching for chimeric transcripts from the RNA sequencing data using existing algorithms. Validation of the work of the studied algorithms for this type of biomaterial has not yet been published.

Analysis of gene fusions from the FFPE material by RNA-seq

The use of DNA and RNA from archival material for genome-wide studies attract researchers’ attention for a long time and it was initially demonstrated that about 80% of genes, expressed in the fresh tissue can be detected in the FFPE samples by microarrays.^229,230 Since then, we have witnessed considerable progress in the field and more recent investigations of RNA from paraffin blocks for gene expression analysis using microarrays²³¹ and sequencing^{228,232–234} revealed sufficient quality of RNA obtained from the FFPE samples to generate reproducible data consistent with RNA from the unfixed material. Furthermore, specific features such as conservation of open reading frame in both fusion partners, presence of RTK domain, and finding of several non-duplicate transcript reads for a fusion were shown as the efficient criteria for discriminating true versus artifact fusion reads in FFPE-derived RNA sequencing data.²³⁵

Indeed, despite the absolute gene expression levels being not necessarily the same, very similar pathways were overrepresented within dysregulated genes obtained from the FFPE and freshly extracted RNA,^232,233 thus demonstrating consistency with IHC studies.^236,237

Fusion transcript detection by RNA-seq from FFPE samples was recently reviewed, focusing on the experimental variables; however, the difference in bioinformatics approaches of fusion transcript detention was not discussed.²³⁸ Gene fusions were detected by RNA sequencing in 7 out of 8 cases of DNA-fusion positive fibrous histiocytomas.²³⁹ Interestingly, comparison of ChimeraScan with TopHat software used in this study revealed better sensitivity of the former (9 versus 5 fusion transcripts detected) suggesting that detailed analysis of the software applications for the fusion transcript detection is needed.²³⁹ It was shown that RNA-seq from the FFPE clinical material detects fusions with 94% (43 out of 46 fusions) concordance with DNA fusions, and one ST7-MET fusion was detected only by RNA-seq.²⁴⁰

Analysis of single-cell RNA sequencing

High-throughput sequencing technology made single-cell RNA (scRNA) or DNA analysis possible at an unprecedented scale. Lately, several consortiums published aggregations and the analysis of the scRNA sequencing data.^241–243

Functional enrichment analysis distinguishes different cell types as well as cancer cells, which also can be distinguished by the mutations and copy number variations typically observed in cancer,²⁴⁴

Importantly, single-cell sequencing characterizes not only cancer cells, but also immune cells infiltrating the tumor. And, analysis of this data might reveal information which is relevant to tumor progression and treatment strategies. For example, sequencing of the T-cell receptor repertoire from glioblastomas treated by vaccination with heat shock protein peptide complex-96 identified dominant T-cell clones that reside in glioblastomas before treatment and stratify patients that are more sensitive to therapy.²⁴⁵

Various methods have been developed to analyze gene expression of single cancer cells and to dissect their molecular subgroups.²⁴⁶ Finding of gene fusions at single cell level can potentially shed light on specific features of cells and their subtypes. Several algorithms for fusion identification in bulk RNA-seq data have been developed,^222,247 but detection of fusions on single cell level is still largely unsolved task. Indeed, the ambiguous and complicated library preparation steps result in generation of artificial chimeric reads and significant increase in the number of false-positive results. Moreover, the probability to detect fusion present in many cells is higher using bulk methods. Thus, several approaches both for sample preparation and for data analyses stages have been proposed. For example, using full-length scRNA-seq method enabled to detected and experimentally verify more gene fusions in scRNA-seq data than in bulk RNA-seq data for HeLa S3 cells.²⁴⁸ These results were congruent with the finding published by another group of authors who could identify well-known as well as potential new fusion in colon cancer samples solely on single cell level.²⁴⁹ However, experimental verification could not be performed here; thus, the increased number of fusions might at least partly represent artifacts of library preparation.²⁴⁹ Another attempt to increase sensitivity of fusion detection at single cell level is to include specific primers targeted for the genes of interest.²⁵⁰ This approach increased sensitivity of BCR-ABL1 detection in chronic myeloid leukemia cells.²⁵⁰ However, this method is suitable only for the analysis of known fusions.

A more recent algorithm called scFusion was published for improving data analysis step.²⁵¹ This method utilizes both statistics and a deep learning model to exclude false-positive results. The algorithm also relies on the hypothesis that cells collected from one sample are more likely to contain the same gene fusions.²⁵¹ This limits its ability to detect rare or low expressed fusions, especially in highly heterogenous samples. Overall, scFusion requires greater sequencing depth as well as sequencing of larger amounts of cells from the same sample for obtaining reliable results. Analysis of the single-cell DNA sequencing of the NSCLC cohort enrolled in the MATCH-R (NCT0251782) trial that developed osimertinib resistance revealed heterogeneity of acquired mutations in the cells including FGFR3-TACC3, KIF5B-RE, and STRN-ALK fusions that can be treated by existing drugs, thus suggesting possible treatments to overcome osimertinib resistance.²⁵² Similarly, RNA sequencing revealed that ALK junctional heterogeneity in NSCLC may predict resistance to crizotinib.²⁵³ Likewise scRNA-seq of chemoresistant cervical cancer revealed induction of the (PI3K)/AKT pathway.²⁵⁴ Thus, it is possible to infer the mechanisms of acquired resistance and to monitor clonal changes of tumors in response to therapy.

Detection of fusions at single cell level can improve distinguishing cells subpopulations, thus shedding light on drug-resistant subclones in a tumor. However, this type of analysis still has strong limitations such as low coverage per individual cells, high PCR amplification bias and lack of standardization in data analysis.

Fusion oncogene databases

The fast growth of gene fusion data necessitates major organizational effort to gather them in the databases, and there is currently nearly a dozen of published databases of cancer fusion genes. Table 4 summarizes the common databases that are specified for fusion genes. One of the first databases designed to catalog gene fusions is the Mitelman database of chromosome aberrations and gene fusions in cancer that was first published in 1994. This database is supplemented with clinical association information that relate cytogenetic and genomic abnormalities, in particular gene fusions, to tumor characteristics or patient prognosis, based either on individual cases or associations. The database is searchable by a wide variety of fields, such as patient age, publication authors, gene, tumor histology, tissue type, mutation recurrence, associated clinical features, and cancer types.²⁵⁵

Table 4.

Databases of gene fusions.

Database name and URL	Description	Database size	Source of data	Reference
Mitelman https://mitelmandatabase.isb-cgc.org	Relates gene fusions and other chromosomal aberrations to tumor characteristics, based either on individual cases or associations	Total number of unique gene fusions 32,962, Total number of genes involved 14,016 as for April 2022	Manual curation of literature	Denomy et al.255
COSMIC: the Catalogue Of Somatic Mutations In Cancer https://cancer.sanger.ac.uk	Catalog of translocations and fusions between gene pairs supplemented with extensive clinical data	19 368 Gene Fusions as for August 2018	Manual curation of literature	Tate et al.,262
TumorFusions: TCGA Fusion Gene Data portal https://tumorfusions.org/	An integrative resource for cancer-associated transcript fusions giving a landscape of cancer-associated fusions using the pipeline for RNA sequencing data analysis	Total fusion called across multiple cancer types is 20731 as of July 2017	Integrated analysis of RNA-seq and DNA-seq data from TCGA	Hu et al.,263 Yoshihara et al.264
ChimerDB http://www.kobic.re.kr/chimerdb/	Fusion transcripts across multiple species knowledgebase and information on cancer breakpoints	67 610 fusion gene pairs, of which 1597 fusion genes with publication support as for November 2019	Sequencing data from TCGA and PubMed publications curation	Jang et al.265
ChiTaRS http://chitars.md.biu.ac.il/	Catalogue of fusion transcripts in humans, mice, fruit flies, zebrafishes, cows, rats, pig and yeast using RNA-seq data	111,582 fusion transcripts in humans, mice, fruit flies, rats, zebrafishes, cows, pigs, and yeast as for 2019	EST and mRNA sequences analysis from NCBI/GenBank and review of publications by Mertens et al. and from the Mitelman collection	Balamurali et al.266
FusionCancer http://donglab.ecnu.edu.cn/databases/FusionCancer/	Fusion gene database derived exclusively from cancer RNA-seq data	11 839 gene fusions as for 2015	Analysis of 591 RNA-seq cancer datasets	Wang et al.267
FARE-CAFÉ http://ppi.bioinfo.asia.edu.tw/FARE-CAFE	Database of functional and regulatory elements in gene fusion events related to cancer	1587 gene fusions as for 2015	Combination of information from different sources, including TICdb, dbCRID, NCBI, and miRTarBase	Korla et al.268
HYBRIDdb http://www.primate.or.kr/hybriddb	Database of hybrid genes in the human genome	3404 gene fusions as for 2007	Data from INSDC about mRNA, EST, cDNA, and genomic DNA sequences	Kim et al.269
FusionGDB 2.0 https://compbio.uth.edu/FusionGDB2/	Fusion Gene annotation DataBase	102 645 fusion genes with 16 146 in-frame as for 2021	Combined data from ChiTaRS 5.0 and ChimerDB 4.0 and manual curation of PubMed articles for the most frequent fusions	Kim et al.270
dbCRID http://dbCRID.biolead.org	Curated database of human chromosomal rearrangements, associated diseases and clinical symptoms	2643 chromosome rearrangements as for 2010	Manual curation of literature	Kong et al.271
TICdb http://www.unav.es/genetica/TICdb/	Finely mapped translocation breakpoints in cancer	1374 fusion sequences from 431 different genes as for 2007	Databases analysis and publications review	Novo et al.272
KuNG FU (KiNase Gene FUsion) http://www.kungfudb.org/	Database containing in-frame kinase gene fusions with intact kinase domain in cell lines	108 total fusions as for January 2021	Manual curation of literature	Kim et al.269

EST, expressed sequence tag; FISH, fluorescent in situ hybridization; TCGS, The Cancer Genome Atlas.

The Mitelman database was followed by COSMIC database in 2004, that started with only four genes,²⁷³ then evolved to contain more than 5 million somatic mutations and more than 19 thousand gene fusions.²⁶²

RNA-seq data is a major source of mining fusion transcripts. Several fusion databases were generated from transcript sequences available datasets, such as TCGA dataset. The Fusion Analysis Working Group identified 25 664 fusion events using RNA-seq data from tumor and normal samples from TCGA using multiple fusion calling tools.²²³ Similarly, TumorFusions database is a searchable portal that catalogues over two thousand gene fusions detected in cancer and normal samples from TCGA.^263,264

ChimerDB is another knowledgebase database of fusion genes^265,274; this database contains fusions identified using bioinformatics analysis of transcript sequences compiled from GenBank and various other well-known public fusion databases and PubMed articles reporting fusion genes. ChimerDB is composed of three modules dealing with the analysis of deep sequencing data (ChimerSeq) and text mining of publications (ChimerPub) with extensive manual annotations (ChimerKB).²⁶⁵

Newer gene fusion database is the ChiTaRS, generated by performing a bioinformatics analysis of transcript sequences and expressed sequence tags (ESTs) for multiple organisms in GenBank, starting from three organisms in the first version,²⁷⁵ then extended to include eight organisms: human, mouse, fruit fly, rats, zebrafishes, cows, pigs, and yeast in the most recent version.²⁶⁶ The latest version includes an extended information about fusions features, as well as 3D chromatin contact maps. In addition, FusionCancer database contains cancer fusion genes deduced from RNA-seq data.²⁶⁷

Other databases combine fusion genes functional, regulatory, and genomic information. Example of such is the FARE-CAFE database that collect various aspects and data concerning fusion genes and proteins as protein domains, domain–domain interactions, protein–protein interactions, transcription factors and microRNAs. FARE-CAFE database incorporates chimeric transcripts and their Genomic information from different resources including Mitelman’s, dbCRID and the Trans location in Cancer (TICdb) databases.²⁶⁸

dbCRID is a comprehensive database of human chromosomal rearrangements events and their associated diseases that documents the type of each event with the related disease or symptoms, the breakpoint positions and other genomic information.²⁷¹ Similarly, TICdb documents the precise location of each breakpoint inside a fusion gene. TICdb gather molecular information on gene fusions resulting from reciprocal translocation events associated with tumors.²⁷²

Aside from cataloging gene fusions, numerous algorithms have been developed to predict fusion candidates from transcriptome data. Data aggregation and functional annotation with visualization support are necessary to assess the reliability, functional significance, and biological roles of predicted fusions. Programs such as GFusion, FusionScan, and STAR-Fusion are thought to be highly sensitive in detecting fusions with less false positives.²⁶⁵ More recently, FusionHub introduced an integrated web platform that supports both annotation and visualization for the largest collection of fusion gene datasets aggregated from 24 resources.²⁷⁶

HYBRIDdb is one of the earliest databases of hybrid genes to use a bioinformatics analysis for identifying gene fusions. HybridDB identified more than 3000 fusions from mRNA, EST, cDNA, and transcript sequences in the NCBI database.²⁶⁹

Kim and Zhou built FusionGDB (Fusion Gene annotation DataBase) that gathers more than 40,000 fusion genes from fusion gene public resources such as TumorFusions and ChiTaRS 3.1. FusionGDB provides extensive functional annotations for these collected fusion events. Most importantly, the gene assessment across pan-cancer fusion genes, open reading frame (ORF) assignment and retention search of protein features.²⁷⁷ FusionGDB was recently updated using deep learning techniques to provide eight categories of annotations: Fusion Gene Summary, Fusion Gene ORF analysis, Fusion Gene Genomic Features, Fusion Protein Features, Fusion Gene Sequence, Fusion Gene PPI analysis, Related Drugs, and Related Diseases.²⁷⁰

Since kinase gene fusions are valuable biomarkers and promising drug targets, specific databases of such fusions have been developed. KuNG FU (KiNase Gene FUsion) is one of the largest curated databases, containing precise annotations on solely in-frame kinase gene fusions with intact kinase domain, which parameters were investigated in cancer cell lines. KuNG FU database is an available and informative tool for facilitating drug development and diagnostic studying.²⁷⁰

Conclusions and further perspectives

Gene rearrangements stand among the major driver mutations in cancer. Recent developments unravel several therapeutically actionable fusion genes with approved targeted drugs effective against hematological and solid tumors. Many additional drugs targeting these and other fusion genes are in clinical trials and in development. Altogether, this gives physicians more options for the choice of therapy. However, cancer genomic instability leads to clonal heterogeneity and selection of cells with resistance to treatment, immune evasion, and metastatic potential, in many cases leading to failure of the therapy. Metastasis is the major problem in oncology. In particular, the picture becomes more complicated by the fact that each metastasis loci represents an individual clone with different mutational profile and drug sensitivity. To choose a proper therapy, it is critically important to provide a physician with actionable information about emerging clones. Thus, the future clinical oncology will utilize methods that will be able to measure mutations in the individual clones and assess the status of immune cells regulating tumor microenvironment. While single-cell sequencing methods that can measure clonal heterogeneity become available for academic research, targeted sequencing, FISH, and IHC remain the working horse of oncology.

To conclude, NGS-based methods provide several advantages for fusion detection both in clinical practice and in research studies. First, they combine fusion detection with finding of other clinically relevant biomarkers, thus providing more relevant information than ‘classical’ methods in just one test, which requires minute amounts of biosample. NGS approaches are suitable for the investigation of different sample types, including cell lines, fresh frozen, and FFPE biopsies. Different computational algorithms have been developed for the analyses of DNA and RNA NGS derived data and obtaining of highly reliable results. Further improvements in fusion detection approaches at both sample preparation and data analysis stages will expand the current knowledge of fusion frequencies among different cancer types, and of their particular impact on tumorigenesis, drug sensitivity, and resistance development. The latter has a strong potential of increasing efficacy of cancer treatment.